Practical Essentials of Intensity Modulated Radiation Therapy, 3 Ed.

1. Delivery of Precision Radiation Therapy: IMRT, IGRT, Proton Beam Therapy

Tim Marinetti  Lei Dong


 In April 1862, Scottish physicist William Thomson, later Lord Kelvin, read a paper to the Royal Society of Edinburgh1 in which he estimated the age of the Earth as between 20 and 400 million years based on classical thermodynamics, assuming a molten body at formation. He was considerably off the mark since his calculations did not include heating from radioactive decay, which was not discovered until the end of the 19th century. The discovery of radioactivity ushered in a revolution in physics, opening up the world of the atom. Ionizing radiation also ushered in a revolution in medicine, being deployed for both diagnostic (e.g., radiographs) and therapeutic purposes. Intensity-modulated radiation therapy (IMRT) has similarly ushered in a new era in radiation oncology. Radiation fields can now be sculpted to target the tumor while sparing healthy tissue in close proximity. Such techniques have been made possible by the enormous leaps made in computing and image processing—not in the least the fast Fourier transform algorithm2—resulting from hardware and software originally developed for the military in the latter part of the 1900s. The explosion of personal computers, smart phones, and video imaging devices (including games) has brought very high-end technology into the mass consumer market. Hence, extremely powerful computers with dedicated graphics processors, along with terabyte data storage, are easily available at minimal cost.

 The term “Intensity-Modulated Radiation Therapy” is used here in a much wider sense than its literal meaning might suggest. Strictly speaking, the use of wedges and customized blocks and bolus to compensate for surface curvature is also intensity modulation. In this book, IMRT denotes a form of three-dimensional conformal radiation therapy (3DCRT) in which a computer-aided optimization process is used to determine customized non-uniform fluence distributions to attain certain specific dosimetric and clinical objectives.

 The previous edition of this book was published in 2005. At that time IMRT was still something of a novelty; 8 years later it is safe to say that the technique has become the de facto standard practice for many tumors. IMRT’s coming of age is described in thorough terms in the 2010 ICRU (hereafter ICRU 83) report which spans 106 pages with over 350 references.3 Readers new to the field will find this report an excellent synthetic introduction to IMRT; experienced practitioners should also note that it contains new reporting guidelines for clinical treatment.

 The use of ionizing radiation for diagnosis and treatment dates back over a century; for a wonderfully concise review of the same, see Bernier et al.4 An enduring clinical problem has been to achieve high levels of irradiation at the tumor site without causing extremely toxic or even fatal consequences in normal tissues in the path of the treatment beam. Advances in technology—especially fast, affordable computers—opened the prospect of true 3D tailoring of radiation fluence with equipment and a planning timescale usable in the clinic. Since its introduction into clinical use,57 IMRT has generated widespread interest. IMRT optimally assigns non- uniform intensities (i.e., weights) to tiny subdivisions of beams, called rays or “beamlets.” The ability to optimally manipulate the intensities of individual rays within each beam permits greatly increased control over the radiation fluence, enabling custom design of optimum dose distributions.

 IMRT can be used to achieve dose distributions that are far more conformal than those possible with standard 3D conformal radiation therapy. These distributions within the Planning Target Volume (PTV), in theory, can be made more homogeneous and, if so desired, a sharper fall-off of dose at the boundary can be achieved. Experience with current IMRT has led to an impression among many that IMRT inherently produces inhomogeneous dose distribution within the target volume. Inhomogeneity commonly observed is the result of the overriding need to protect, partially or wholly, one or more critical organs. In other words, the dose distributions tend to be more heterogeneous because the homogeneity criterion is considered less important than the normal-structure avoidance criterion. If all things were equal, the IMRT plan always should produce a more homogeneous dose distribution than produced by a plan made with uniform beams.

 IMRT is not limited to delivery of uniform dose to a single target. Taking advantage of the pencil beam optimization technique, IMRT allows simultaneous delivery of different dose levels to different target volumes, and the target volumes may be spatially separated (such as in the case of multiple brain lesions).

 The new ICRU report 83 brings in some important conceptual changes. The previous ICRU reports for photon therapy (No. 50 in 1993, and its supplement No. 62 in 1999) defined a reference point within the treatment volume, one which was easily located with anatomical landmarks and where dosage could be accurately measured. The prescription dose was something of an ideal to be sought. However, with IMRT, treatment volumes are more relevant, and doses are given and bounds set within those volumes. Also, the prescription dose itself is now taken to be the final result of treatment planning by physicists and radiation oncologists. With literally thousands of degrees of freedom available to the planner, the dosage to a single point is no longer adequate to describe or evaluate a plan.

 ICRU 83 cites the increase in the clinical impact and use of IMRT in the recent past: “In a survey performed in 2003 in the United States, among 168 radiation oncologists randomly selected, one-third was using IMRT. In 2005, a similar survey showed that more than two-thirds of radiation oncologists were using some form of IMRT, mainly for increased normal tissue sparing or target-dose escalation.”3,8,9Another noteworthy metric is the number of research reports in the peer-reviewed literature concerning IMRT. A scan of the Web of Science database showed full papers with IMRT in the title numbering less than 50 per year till 2001. It breached the 100-per-year mark in 2005 and has plateaued at about 140 for the years 2009–11. IMRT is fast becoming the standard practice in delivering radiation treatment.

 Both in concept and execution IMRT is intimately tied to 3D imaging. As ICRU 83 states, “Three-dimensional CRT, in general, and IMRT, in particular, increase the need for accurate anatomic delineation. This requires an adequate specification of the tumor location and a thorough knowledge of the processes of likely infiltration and spread.”3

1.1. Features and Benefits of Intensity-Modulated Radiation Therapy

1.1.1. Dose Conformality and Multiple Simultaneous Treatments

 The biggest advantage of IMRT is the ability to produce much higher conformality of dose distributions than that achievable with conventional 3DCRT using uniform beam intensities. In particular, IMRT can produce concave-shaped isodose distributions that may more closely follow the shapes and boundaries of the target and other critical structures in three dimensions. In contrast, the isodose distributions designed for 3DCRT plans are convex, which may be suboptimal in treating certain disease sites. These improved dose distributions potentially may lead to improved tumor control and reduced normal tissue toxicity. For example, tumors of the head and neck often require concave-shaped treatment volumes in order to spare the closely adjacent sensitive critical structures (e.g., brainstem, spinal cord). Such fluence distributions are easily achieved with IMRT, but may be difficult or impossible to achieve by other techniques, including 3DCRT. This is illustrated in Figure 1-1, taken from the ICRU report.

 In addition, IMRT has the potential to be more efficient with regard to treatment planning and delivery than standard 3DCRT, although gains in this direction are being realized rather slowly. The treatment design process is relatively insensitive to the choice of planning parameters, such as beam directions.1012 There are no secondary field-shaping devices other than the computer-controlled multileaf collimator (MLC). Furthermore, large fields and boosts can be integrated into a single treatment plan, and, in many cases, electrons can be dispensed with, permitting the use of the same integrated boost plan for the entire course of treatment.13,14 An integrated boost treatment may offer an additional radiobiologic advantage15 in terms of lower dose per fraction to normal tissues while delivering higher dose per fraction to the target volume. Higher dose per fraction also reduces the number of fractions and hence lowers the cost and burden on the patient for a treatment course. IMRT also offers the potential for adaptive therapy—revision of the treatment plan according to imaging of tumor reduction and organ movement during the course of RT. Castadot et al.16 recently reviewed adaptive therapy in head and neck cancer. Mechalakos et al. presented a case study where weekly cone-beam CT was used to track the treatment of a recurrent neck mass from a nasopharyngeal cancer.17

1.1.2. Normal-Organ Sparing

 The ability to shape dose distributions can be exploited to create sharp dose fall-off near the boundaries of the target and other critical structures. A sharper fall-off of dose at the PTV boundary, in turn, means that the volume of normal tissues exposed to high doses may be reduced significantly. These factors may allow escalation of tumor dose, reduction of normal tissue dose, or both, hopefully leading to an improved outcome. A lower rate of complications also may mean lower cost of patient care following the treatment.

FIGURE 1-1. Comparison of CRT (left) and IMRT (right). The ability for CRT to alter isodose lines was limited to shaping of field boundaries with MLCs or blocks, and the use of wedges or compensators for missing tissues and central blocks for shielding critical structures. The IMRT beams can have highly non-uniform beam intensities (fluences) and are capable of producing a more concave-shaped absorbed-dose distribution. With neither conformal therapy nor IMRT can the PRV be always completely avoided, but with IMRT the concave isodose curve that includes the PTV better avoids the PRV. The black region indicates the PTV; the gray region indicates a PRV, and the line surrounding the PTV is a typical isodose contour. (Reprinted from International Commission on Radiation Units & Measurements [ICRU] Report No. 83 in J ICRU 2010:10(1), with permission.)

 To illustrate the dosimetric advantage of IMRT over conventional 3DCRT techniques, Figure 1-2 shows the isodose curves for four different RT modalities: two conventional 3DCRT (with four and seven fields, respectively) and two IMRT plans, one using serial tomography and the second with a ten-field step-and-shoot MLC. Note the better conformality in the IMRT plans.

FIGURE 1-2. Typical isodose distributions for treating prostate cancer from (A) a four-field three-dimensional conformal radiation therapy (3DCRT) plan (4F-CRT), (B) a seven-field 3DCRT plan (7F-CRT), (C) an intensity-modulated radiation therapy plan delivered by serial tomotherapy using MIMiC (NOMOS Corp., Sewickley, PA), and (D) a ten-field step-and-shoot segmental multileaf collimator (SMLC) plan.

1.2. Limitations and Risks of Intensity-Modulated Radiation Therapy

 We should recognize, however, that IMRT has limitations. There are many dose distributions (or dose–volume combinations) that are simply not physically achievable. Furthermore, our knowledge of what is clinically optimal and achievable and how best to define clinical and dosimetric objectives of IMRT is often limited. Moreover, the best solution may elude us because of the limitations of the mathematical formalism used or because of the practical limits of computer speed and hence the time required to find it.

 Uncertainties of various types (e.g., those related to daily, or interfraction, positioning; displacement and distortions of internal anatomy; intrafraction motion; and changes in physical and radiobiologic characteristics of tumors and normal tissues during the course of treatment) may limit the applicability and efficacy of IMRT. Dosimetric characteristics of a delivery device, such as radiation scattering and transmission through the MLC leaves, introduce some limitations in the accuracy and deliverability of IMRT fluence distributions. In addition, the limited spatial and temporal coverage and overall accuracy of the current IMRT dosimetric verification systems (based principally on radiographic and radiochromic film) diminish the confidence in the delivered dose. Furthermore, most current dose calculation models are limited in their accuracy, especially for the small, complex shapes required for IMRT.

 It is quite conceivable that inaccuracies in dose calculations may yield a solution different from the one derived if dose calculations were accurate. Perhaps the most important factor that may limit the immediate success of IMRT is the inadequacy of imaging technology to define the true extent of the tumor, its extensions, and radiobiologic characteristics as well as the geometric, dose–response, and functional characteristics of normal tissues.

 We should also be aware of the risks of IMRT. The effect of large fraction sizes used in integral boost IMRT on tissues embedded within the gross tumor volume (GTV) is uncertain and may present an increased risk of injury.18There may also be an increased risk that improper use of spatial margins, coupled with the high degree of conformality with IMRT, may lead to geographic misses of the disease and recurrences, especially for disease sites where positioning and motion uncertainties play a large role or where there are significant changes in anatomy and radiobiology during the course of radiotherapy. Similarly, high doses in close proximity to normal critical structures may pose a higher risk of normal tissue injury. In addition, IMRT dose distributions are unusual and highly complex and existing experience is too limited to interpret them properly and evaluate their efficacy. Finally, while IMRT can spare specific tissues as compared to conventional RT, the use of many more beams and irradiation angles means that a larger volume of normal tissue is exposed, albeit to lower accumulated doses.

1.3. Intensity-Modulated Radiation Therapy Procedures

1.3.1. Various Intensity-Modulated Radiation Therapy Implementations

 During the past 15 years, a variety of techniques have been explored for designing and delivering optimized IMRT.6,7,10,12,1847 Many of these are implemented in commercial IMRT systems. The most significant differences among the various approaches are in terms of the mechanisms they use for the delivery of non-uniform fluences. Although the merits of each are often speculated, the superiority of any of the approaches over the others is difficult to assess because there have been no systematic comparisons of clinical treatment plans.

 Of the various approaches proposed, two dominant but significantly different methods have emerged. Mackie et al.33,48 proposed an approach called tomotherapy in which intensity-modulated photon therapy is delivered using a rotating slit beam. A temporally modulated slit MLC is used to rapidly move leaves into or out of the slit. Like in a computed tomography (CT) unit, the radiation source and the collimator continuously revolve around the patient. The patient is translated either between successive rotations (serial tomotherapy) or during rotation (helical tomotherapy). For helical tomotherapy, the system looks like a conventional CT scanner and includes a megavoltage portal detector to provide for tomographic reconstruction of the delivered dose distribution. Arc therapy has been reviewed by Palma et al.49

 A commercial slit collimator (called MIMiC) of the type proposed by Mackie et al.33 has been designed and built by NOMOS Corporation presently Best nomos, Pittsburgh, PA. The MIMiC collimator was incorporated into their serial tomotherapy system, known as Peacock,25,26 for planning and rotational delivery of intensity-modulated treatments.50 Modern tomotherapy machines are marketed by Accuray Inc.

 In the second approach, implemented first into clinical use at Memorial Sloan-Kettering Cancer Center,6,7,34,36,42,43,51 a standard MLC is used to deliver the optimized fluence distribution either in dynamic mode (defined as the leaves moving while the radiation is on) or in static mode, that is, the “step-and-shoot” mode (defined as sequential delivery of radiation subportals that combine to deliver the desired fluence distribution), to deliver a set of intensity-modulated fields incident from fixed gantry angles. These techniques are gaining wide acceptance rapidly. Every major commercial treatment planning system manufacturer has implemented one or both of these approaches. Figure 1-3 shows a multileaf collimator and a cutaway view of a modern linac machine.

 A third approach, called intensity-modulated arc therapy or IMAT, developed by Yu,47 uses a combination of dynamic multileaf collimation and arc therapy. The shape of the field formed by the MLC changes continuously during gantry rotation. Multiple superimposing arcs are used, and the field shape for a specific gantry angle changes from one arc to the next appropriately in such a way that the cumulative fluence distribution of all arcs is equal to the desired distribution. In 2007, a new form of arc therapy, termed “Volumetric-Modulated Arc Therapy (VMAT),” was proposed. In VMAT, the dose rate, gantry speed, MLC leaf positions, and optionally the collimator angles are allowed to dynamically vary during treatment delivery.52 Arc therapy is discussed in a recent review and comparison of intensity-modulated techniques.53

 In addition to these approaches, the University of Michigan has used the so-called multisegment approach in which each of a number of beams is divided into multiple segments.54 One segment of each beam frames the entire target while the others spare one or more normal structures. Each segment is uniform in intensity. The weights of segments of all beams are optimized to produce the desired treatment plan. The treatments are delivered as a sequence of multiple uniform field segments. A similar approach was proposed previously by Mohan et al.55 In almost all of these significantly different treatment delivery approaches, the underlying principles of optimization are similar although the specifics may be quite different.

 Intensity-modulated proton therapy (IMPT). A scanning proton pencil beam can be produced and delivered in a way similar to that in photon IMRT. Different from the linear accelerator–based photon machine (using electrons to produce Bremsstrahlung x-rays and multileaf collimators to form pencil photon beams), pencil proton beams can be directly produced by either a synchrotron or a cyclotron, and the pencil beam can be steered by a scanning magnetic field. In order to deliver IMPT, both the intensity and the energy of the proton pencil beam need to be variable. The energy of the proton beam determines the depth of the Bragg peak at which the proton beam delivers the highest dose to the treatment target. An IMPT clinical example was described by Lomax et al.80

1.3.2. Intensity-Modulated Radiation Therapy Process Overview

 As mentioned previously, there are significant differences in concepts and processes between 3DCRT and IMRT. However, there are also many similarities. In particular, IMRT relies on many of the same imaging, dose calculations, plan evaluation, quality assurance (QA), and delivery tools as does 3DCRT.

 The IMRT process consists of several phases: planning, quality assurance (QA), and delivery of radiation with either static or dynamic MLC. The steps of IMRT optimization during planning are shown in Figure 1-4. The tomotherapy process is similar, except that the fixed-beam angle selection is replaced by selection of the slice thickness and for serial tomotherapy, by selection of the gantry rotation angles.

 In the preparatory phase of the IMRT process, volumes of interest (such as tumors and normal organs) are delineated on 3D CT images,56 often with assistance from other co-registered imaging modalities. The second imaging technique most often used is magnetic resonance imaging (MRI); the latter has an advantage over CT in that it can provide both structural and physiologic information.57 Other imaging modalities such as positron emission tomography (PET) use intrinsic or externally added molecular markers to visualize specific metabolic processes or cellular phenotypes.5862 Variations in target contouring between physicians can be significant, and efforts at computer-assisted methods have been reported, for example, by Chao et al.,63 using deformable image registration. Hong et al.64 recently reviewed target volume contouring practices in head and neck cancer patients at 20 institutions and found significant heterogeneity. The RTOG has devoted considerable effort to its online contouring atlases for various disease sites.

FIGURE 1-3. Left, a multileaf collimator viewed along its beam axis. Right, cutaway view of the gantry head of Clinac ® RT machine. Photos courtesy of Varian Medical Systems.

FIGURE 1-4. Comparison between traditional (left) and IMRT (right) optimization processes. (Reprinted from J ICRU 2010: 10(1), with permission.)

 The desired objectives in the form of an objective function, its parameter values, and the IMRT fractionation strategy are then specified, and beam configuration is defined. Typically the objective function65assigns a weighted “cost” to the square of the difference between the desired 3D fluence distribution and that calculated at a given iteration. The software attempts to minimize the costs—maximizing dosage to the tumor volume and minimizing exposure of normal tissues.

 In the treatment plan optimization phase, an iterative process is used to adjust and set the intensities of rays of each beam (or portion of the arc) so that the resulting intensity distributions yield the best approximation of the desired objectives. The IMRT plan is then evaluated to ensure that the trade-offs made by the optimization system are acceptable. If further improvement is deemed necessary and possible, the objective function parameters are modified and the optimization process repeated until a satisfactory treatment plan is achieved.

 In the leaf sequence–generation phase, the intensity distributions are converted into sequences of leaf positions. It is conceivable that certain dose distributions cannot be delivered as a result of the leakage characteristics of the delivery devices. Therefore, in most treatment-planning systems, the leaf sequences are used in a reverse process to calculate the dose distributions they are expected to deliver. These dose distributions, called the deliverable dose distributions, are evaluated for clinical adequacy. If necessary, objective function parameters are further adjusted to produce an intensity distribution that leads to a deliverable dose distribution that meets the desired objectives. This is the practice in most systems. However, in some systems the leaf sequence–generation process is incorporated into the IMRT plan optimization loop so that the optimized and deliverable dose distributions are identical. The leaf sequences then are transmitted to the treatment machine and used to verify whether the dose distribution that will be delivered to the patient is correct and accurate. The patient is then set up in the usual fashion and treated. In general, the entire treatment is delivered remotely without the need to reenter the treatment room in between fields.


 Because IMRT is an emerging technology, it is essential to know the definitions of some of the important nomenclature used in IMRT planning and delivery.

 Monitor Unit (MU): the dosage delivered at the central axis of the treatment beam. Linear accelerator (linac) machines are typically calibrated so that 1 MU equals 1 cGy.

 Beamlet (bixels or rays): The beamlet is a small photon intensity element used to subdivide an intensity-modulated beam for the purposes of intensity distribution optimization or dose calculation. Sometimes, the beamlets are also called “bixels,” “rays,” or “pencil beams.” The intensity of the beamlet can be expressed in units of either particle fluence or energy fluence, depending on the particular dose-calculation algorithm used.

 Dynamic Multileaf Collimator (DMLC): The DMLC is an IMRT delivery mode in which the leaves continuously move and shape the beam intensity while the radiation is turned on. This mode has the advantage of higher spatial resolution and quick treatment delivery; however, it requires more accurate synchronization of leaf positions with beam-on time. Typical examples of this mode are the sliding-window technique using conventional multileaf collimators (MLCs) and the NOMOS MIMiC system using binary collimators in the arc treatment mode.66,67

 Static Multileaf Collimator (SMLC): The SMLC is an IMRT delivery mode in which the leaves only move when the radiation beam is turned off and remain in their predefined positions while the required radiation doses are being delivered. This is usually called the “step-and-shoot” delivery technique.68,69

 Segment: This is the shaped aperture (usually formed by MLCs) with uniform beam intensity. A segment is the basic unit in an SMLC treatment delivery. Sometimes a segment is also called a “control point.”

 Objective Function: This function is a mathematic formalism of clinical requirements. These clinical requirements can be (a) dose based, such as the minimum or maximum dose for a target or critical structure; (b) dose–volume based, which specifies a fractional volume that can receive certain doses; and (c) dose–response based, which usually uses limited clinical data to translate the dose requirements into certain clinical outcomes. These clinical indicators (outcomes) may include, but are not limited to, tumor control probability, normal tissue complication probability, and equivalent uniform doses.

 Score (cost): The score is a numeric value of the objective function that represents a figure of merit indicating the quality of a treatment plan. The numeric score is a key parameter for IMRT optimization.

 Inverse Planning: This is the term used to describe an optimization process that translates the mathematic formalism of clinical requirements into deliverable intensity patterns. The mathematical problem of optimization has been reviewed by Ehrgott et al.70 Although the word “optimization” is used, it does not always guarantee that a global optimal solution is found for the clinical problem. The numeric solution can be trapped into a local minimum during the optimization process. The solution is also subject to the limitations of the mathematical description and parameterization of the clinical problem. A flawed objective function can lead to erroneous or clinically inferior solutions.

 Forward Planning: Forward planning is a trial-and-error process in which the treatment fields or beam weights are modified iteratively (usually manually) to achieve acceptable clinical solutions. The process is commonly used in designing 3DCRT plans, although the same process can be used to create simple “field-in-field” intensity-modulated treatments. Sometimes a simple beam-weight optimization may be used in the forward planning process, but a forward planning process, in general, does not take full advantage of mathematic optimization using beamlets. Because of the limitations in current IMRT planning systems, the forward planning process can be effective in solving certain clinical problems. For example, the “field-in-field” planning technique has been used successfully in designing breast cancer radiation treatments in which the goal was to improve the dose uniformity in a parallel-opposed beam arrangement.71,72 There is also reported success in head and neck and prostate treatments, but, in general, the solutions are inferior as compared with the inverse-planning approach.73,74

 Leaf Sequencing and Deliverable Optimization: The leaf sequence is a set of leaf positions and the corresponding monitor units (MUs) (beam-on time) to be delivered by the treatment machine. In some IMRT planning systems, the optimization process is separated into two sequential steps. The first step is to generate the ideal fluence patterns that satisfy the optimum solution for the objective function. Then the ideal fluence patterns are converted into deliverable leaf sequences in the second step. If the optimization process does not take into account some of the limitations of the deliverable system (e.g., leakage and head scatters), there will be a degradation in the final deliverable treatment plan. Sometimes the optimization process can create leaf sequences that are not physically deliverable. A deliverable optimization process takes into account the constraints and characteristics of the leaves and reoptimizes leaf sequences on the basis of deliverable fluence distributions and the original objective function.75,76Deliverable optimization will improve the quality or the deliverability of an IMRT plan.

 Aperture-based IMRT: To improve deliverability and reduce beam-on time, a subset of IMRT solutions is sought by dividing the treatment portal into predefined segments or apertures. The rule of aperture segmentation varies depending on differences in implementations.7779 When the beam apertures are defined, the optimization process becomes a standard beam-weight optimization, which can be solved quickly. Because aperture-based IMRT does not take full advantage of beamlet optimization, the solution is usually inferior to that of a pencil beam–based inverse-planning algorithm.

 Class Solution: This involves the use of historical experience based on solving similar cases with the same treatment technique or the same approach. Typical IMRT class solutions include a set of fixed gantry angles or a set of partial volume dose prescriptions for a particular treatment site. A clinical protocol, such as the Radiation Therapy Oncology Group (RTOG) phase I and II study of conformal and IMRT treatments for oropharyngeal cancer (RTOG H-0022,, contains many important clinical guidelines, but may not include sufficient treatment directives for actual IMRT planning. An example of a class solution for ethmoid sinus cancer was described by Claus et al.81

 Multimodality Image Fusion: As target delineation becomes an important step in IMRT planning, the use of two or more imaging techniques to provide additional spatial or functional information about the treatment target(s) has become common. Image fusion refers to a process that combines two or more signals from different images of the same subject into one single dataset. In most situations, the additional information from the fused image is transcribed to the computed tomography image dataset as contoured structures, which will be used for IMRT planning.

 Image Registration: This is a process to find the geometric transformation that brings one image in precise spatial correspondence with another image. Image registration is frequently used in both single modality and multimodality image fusion.

 Digital Imaging and Communications in Medicine (DICOM): This is a standard protocol for medical image transmission and management ( Most diagnostic images are transferred from one system to another by using the latest DICOM 3 protocol, maintained by the combined efforts of the ACR and the National Electrical Manufacturers Association. The latest standard can be obtained at ftp://medical

 DICOM RT: This is the RT extension of the DICOM protocol. The current RT objects include, but are not limited to, the RT structure set (contours are delineated from the images for each named structure), RT image (e.g., digitally reconstructed radiographs and portal images), RT plan (e.g., treatment parameters, including gantry angles, collimator settings, MUs, and MLC leaf positions), and RT dose (calculated dose matrix).


3.1. Delineation of Target Volume and Critical Structures

 ICRU 83 presents updated definitions for the assorted volumes, which will form the skeleton of the treatment plan.3 Conceptually, the volumes contain three types of tissue: (a) malignant lesion; (b) otherwise normal tissue near the tumor which is already or likely to be infiltrated by microscopic disease; and (c) more distant normal issue and organs. The quoted definitions which follow are from ICRU 83.3

 Gross Tumor Volume (GTV): “The GTV is the gross demonstrable extent and location of the tumor. The GTV may consist of a primary tumor (primary tumor GTV or GTV-T), metastatic regional node(s) (nodal GTV or GTV-N), or distant metastasis (metastatic GTV, or GTV-M).” They note that in some cases it may not be possible to differentiate expanding primary lesions from nearby metastatic disease. The GTV for IMRT is always defined from anatomical images, usually CT with or without MRI, and increasingly supplemented by PET.

 Clinical Target Volume (CTV): “The CTV is a volume of tissue that contains a demonstrable GTV and/or subclinical malignant disease with a certain probability of occurrence considered relevant for therapy. There is no general consensus on what probability is considered relevant for therapy, but typically a probability of occult disease higher than from 5% to 10% is assumed to require treatment.” The volumes outside the GTV encompassed by the CTV will depend a great deal on the particular tumor, for example, with high or low propensity for lymph node extension. In the past, the CTV was effectively the GTV (including affected nodes) plus a 1- to 2-cm margin. The current definition stresses more the physiologic criteria based on specifics of disease spread for each tumor. Gregoire and co-workers have compiled studies on CTV margins into a book.82 In postoperative situations, following an R0 or R1 resection, there is no gross tumor so only the CTV need be defined. Readers are strongly encouraged to consult the ICRU 83 report for details.

 Planning Target Volume (PTV): “The PTV is a geometrical concept introduced for treatment planning and evaluation. It is the recommended tool to shape absorbed-dose distributions to ensure that the prescribed absorbed dose will actually be delivered to all parts of the CTV with a clinically acceptable probability, despite geometrical uncertainties such as organ motion and setup variations.”

 Organ at Risk (OAR): “The OAR or critical normal structures are tissues that if irradiated could suffer significant morbidity and thus might influence the treatment planning and/or the absorbed-dose prescription. In principle, all non-target tissues could be OARs. However, normal tissues considered as OARs typically depend on the location of the CTV and/or the prescribed absorbed dose.” All normal tissue exposed to radiation during treatment is at risk, but the OAR is generally taken to be rather more specific—structures in the immediate vicinity of the PTV, sparing of which may demand specific recontouring of the CTV or PTV. Historically OARs have been loosely grouped into “serial,” or “parallel” organs or a combination of the two, following the work of Withers and co-workers using the concept of functional subunits in each organ.82,84,85 Serial organs, such as spinal cord, can suffer unacceptable damage even if only a small portion is irradiated, whereas parallel organs such as liver can suffer loss of a portion without total loss of function.

 Planning Organ at Risk Volume (PRV): “As is the case with the PTV, uncertainties and variations in the position of the OAR during treatment must be considered to avoid serious complications. For this reason, margins have to be added to the OARs to compensate for these uncertainties and variations, using similar principles as for the PTV. This leads, in analogy with the PTV, to the concept of PRV.” As with the OAR itself, margins in the PRV will be affected by the serial or parallel attributes of the adjacent tissues.

 Remaining Volume at Risk (RVR): “The RVR is operationally defined by the difference between the volume enclosed by the external contour of the patient and that of the CTVs and OARs on the slices that have been imaged.” Definition of an RVR and its inclusion in the treatment plan (at least in the form of dose constraints) are essential in IMRT. Without such limits, the optimization software could craft excellent dose distributions for the CTV and OAR, but cause toxic irradiation levels in otherwise uncontoured tissues.

 Treated Volume (TV): “The TV is the volume of tissue enclosed within a specific isodose envelope, with the absorbed dose specified by the radiation oncology team as appropriate to achieve tumor eradication or palliation, within the bounds of acceptable complications.” The TV is what is physically deliverable given the limitations of beam collimation and homogeneity, and, more importantly, the risks of treatment-associated morbidity acceptable to the oncologist and the patient. ICRU 83 proposes that, in conformity with its proposal for proton therapy, the TV be defined as the dosage received by 98% of the PTV. This serves as a measure of the minimum absorbed dose and is also referred to as Dnear-minimum. In an analogous manner, a Dnear maximum is defined as D2%, the dose received by 2% of the PTV receiving the highest fluence. Readers are referred to Section 3 of the ICRU report.

 It should be noted that the GTV, CTV, and OAR represent volumes based on anatomical and physiologic judgments of the location of malignant growths, or of normal tissues in danger from metastatic spread and/or treatment-induced toxicity. These are independent of the particular irradiation protocol employed (viz., 3DCRT, IMRT, or particle beams). The PTV, PRV, and TV are intimately tied to the specific radiation therapy used.

 The optimization process considers explicitly and simultaneously both the gross disease and the larger volumes of occult or microscopic disease to design an IMRT plan. A supplementary margin is added to allow for uncertainties related to the movement of the tumor volume from one day to the next and for intrafraction motion to obtain the PTVs. The number of normal structures that need to be drawn also increases. In conventional radiotherapy, in which the use of large uniform fields is typical and the treatment plans are evaluated manually, a clinician can make a reasonable estimate of the dose received by a volume of interest even if it is not explicitly drawn. In IMRT, in which dose is being escalated to unprecedented levels, where dose distributions are highly non-uniform, and where plans are generated and evaluated by the computer during the iterative optimization process, all structures to which the dose must be constrained need to be delineated. Examples of such target delineations will be given graphically in the subsequent chapters on each organ system.

 IMRT is also called “image-guided” radiotherapy because the use of volumetric image information is almost compulsory. Due to the steep dose gradients employed, the success of IMRT depends crucially on the accurate determination of the target volume and critical organs at risk (OAR). Besides set-up errors, there will be changes in the location and volume of tumors and normal tissues during the course of treatment.

 Multimodality imaging can enhance our knowledge about the extent that diseases should be treated. In addition, and especially for IMRT, imaging is being incorporated into the treatment course to adjust the radiation fields to the actual positions of target volumes and OAR.

3.2. Treatment Planning and Optimization

 The core of IMRT is in the treatment planning and optimization processes. The planner can vary the amount of irradiation administered on a given day of the treatment course and the way that is distributed. The latter can be adjusted by altering the fluence (with an MLC) and the direction of the radiation source (beam angle). These processes translate clinical requirements of a specific clinical problem into machine-deliverable commands.

3.2.1. Fractionation/Treatment Schedule

 In principle, conventional fractionation strategies can be used to design IMRT plans as well. For example, with a strategy similar to the conventional 1.8-Gy to 2-Gy/fx schedule, a major portion of the dose could be delivered in the initial phase using uniform fields designed with standard 3D conformal methods followed by an IMRT boost. Alternatively, separate IMRT plans could be designed for both the initial large-field treatment and the boost treatment. It may be intuitively obvious that, if a large portion of the dose has already been delivered using large fields, it may be very difficult, if not impossible, to achieve a high level of dose conformation with the remaining fractions in the IMRT-boost phase.18

 As indicated earlier in this chapter, IMRT may be most conformal if all target volumes (gross disease, subclinical extensions, and electively treated nodes) are treated simultaneously using different fraction sizes.18 Such a treatment strategy has been called the simultaneous integrated boost.13,14,18 Mackie and co-workers had also indicated the possibilities for irradiation boost in their first paper on serial tomography.33 The SIB IMRT strategy not only produces superior dose distributions; it is also an easier, more efficient, and perhaps less error-prone way of planning and delivering IMRT because it involves the use of the same plan for the entire course of treatment. Furthermore, in many cases, there is no need for electron fields and the nodal volumes can be included in the IMRT fields; thus the perennial problem of field matching86 encountered in the treatment of many sites is thereby avoided.

 Because each of the target regions receives different doses per fraction in the SIB IMRT strategy, prescribed nominal (physical) dose and dose per fraction must be adjusted appropriately. The adjusted nominal dose and fraction size for each target region depends on the number of IMRT fractions. The fraction sizes may be estimated using an isoeffect relationship based on the linear–quadratic model and the values of its parameters (such as α/β ratios, tumor doubling time).

 The effect of the modified fractionation on acute and late toxicity of normal tissues both outside and within the volumes to be treated also should be considered. Because of the improved conformality of IMRT plans, dose to normal tissues outside the target volume is typically lower than for conventional treatment plans. In addition, if the number of fractions is larger than the number of fractions used to deliver large fields in conventional therapy, the dose per fraction to normal tissues is lower. Therefore, the biologically effective dose would be lower still. However, normal tissues embedded within or adjacent to the target volumes would receive high doses per fraction and may be at higher risk. Isoeffect formulae for normal tissues also may be derived to estimate the effect of a particular fractionation strategy (see ICRU 83, pp. 36–38). These formalisms would need to incorporate regeneration and change in sensitivity over the treatment course.

 The values of parameters for the computation of altered fractionation may, in theory, be obtained from published studies. Studies by Maciejewski et al.87 and Withers et al.,8890 for example, have yielded important information for estimating tumor parameters for head and neck carcinoma. In general, the data available are limited. Furthermore, there is considerable uncertainty in the data, and there are concerns about the validity of the numerous assumptions in the linear–quadratic model and the isoeffect formalism, especially with regard to normal tissues. (For an early review of the linear–quadratic model see Fowler.91) Much of the accumulated data on normal tissue complications comes from clinical experience in the era of wide field RT, so the dosage limits reported from such studies may not be immediately applicable to IMRT. Nevertheless, various investigators have carried out the necessary calculations and adopted SIB IMRT fractionation strategies. Continued investigations and clinical trials are needed to develop more reliable time–dose fractionation models, to produce better estimates of their parameters and to evaluate alternate SIB IMRT fractionation strategies for all sites.15 The following are some examples of IMRT fractionation strategies that have been used for IMRT of head and neck cancers.

 In Radiation Therapy Oncology Group H-0022 protocol for early stage oropharyngeal cancer, 30 daily fractions (5 per week × 6 weeks) are used to simultaneously deliver 66 Gy (2.2. Gy per fraction) to the PTV, 60 Gy (2 Gy per fraction) to the high-risk subclinical disease (“levels II–IV bilaterally, Ib ipsilaterally, and level V and retropharyngeal nodes if the jugular nodes were involved”), and 54 Gy (1.8 Gy per fraction) to subclinical disease. These are biologically equivalent to 70, 60, and 50 Gy, respectively, if given in 2 Gy per fraction. For normal structures, brainstem, spinal cord, and mandible are maintained below 54, 45, and 70 Gy, respectively. The mean dose to the parotid glands is maintained below 26 Gy and/or 50% of one of the parotids is maintained below 30 Gy and/or at least 20 mL of the combined volume of both parotids is constrained to receive no more than 20 Gy. Sixty-nine patients were accrued at 14 institutions. Treatment-associated xerostomia improved following therapy, in contrast to regular RT. High locoregional control was achieved with stringent adherence to protocol guidelines.92

 The SIB strategy at Virginia Commonwealth University involves a dose-escalation protocol in which primary nominal dose levels of 68.1, 70.8, and 73.8 Gy, given in 30 fractions (biologically equivalent to 74, 79, and 85 Gy, respectively, if given in 2 Gy per fraction) are used.93 Simultaneously, the subclinical disease and electively treated nodes are prescribed 60 and 54 Gy, respectively (biologically equivalent to 60 and 50 Gy, respectively, if given in 2 Gy fractions). Spinal cord and brainstem are maintained below 45 and 55 Gy, respectively, and an attempt is made to allow no more than 50% of at least one parotid to receive higher than 26 Gy.

 At the Mallinckrodt Institute of Radiology, the SIB strategy for definitive IMRT prescribes 70 Gy in 35 fractions in 2 Gy per fraction to the volume of gross disease with margins. The adjacent soft tissue and nodal volumes at high risk are treated to 63 Gy in 1.8 Gy per fraction, and simultaneously 56 Gy in 1.6 Gy per fraction is given to the elective nodal regions. This regimen has been shown to be well tolerated when combined with concurrent chemotherapy.94

 A concise review of altered fractionation in treatment of head and neck cancer has recently been presented by Mendenhall et al.95

 Normal tissue dose limits based on recent clinical experience, RTOG protocols, and the QUANTEC recommendations are presented in Chapter 4.

3.2.2. Beam Configuration Systems Using Fixed Intensity-Modulated Fields

 The beam configuration can have a significant impact on the quality of an optimized IMRT plan. It may be argued that, because of the greater control over dose distributions afforded by optimized intensity modulation, the fine-tuning of beam angles may not be as important for IMRT as it is for standard radiotherapy. However, optimization of beam angles may find paths least obstructed by critical normal tissues, thus facilitating the achievement of the desired distribution with a minimum of compromise.

 Beam angle optimization, however, is not a trivial problem. There have been some attempts to solve this problem,9698 and the recent advances in mathematical operations research applied to the problem have been reviewed recently.70 To appreciate the magnitude of the problem, consider the following example. If the angle range is divided into 5-degree steps, nearly 60,000 combinations would need to be tested for three beams, nearly 14 million combinations for five beams, nearly 1.5 billion combinations for seven beams, and so on. Considering the magnitude of the search space, none of the optimization methods is likely to be able to demonstrate a significant improvement in treatment plans, let alone find a truly optimum combination when the number of beams is five or more.

 Furthermore, the beam angle optimization problem is known to have multiple minima,99 which means that fast gradient-based optimization techniques may fail. Stochastic methods100,101 in principle should avoid the local minimum problem, but may present excessive computing time demands. This should prove less of a problem in the near future, especially with the use of dedicated parallel processors which can drastically reduce computation time. For a review see Pratx.102

 Another question that may be asked is how many beams are optimal. In principle, a larger number of beams would provide a larger number of parameters to adjust and therefore a greater opportunity to achieve the desired dose distributions. (Thus, in theory, a rotational beam would be the ultimate.) However, for fixed-beam IMRT, it may be desirable to minimize the number of beams to reduce the time and effort required for planning, QA, dosimetric verification, and delivery of treatment. Fewer intensity-modulated beams would be needed if beam angles were optimized than if the beams were placed at equiangular steps. Calculations by Webb11 indicate that 7 or 9 fields give adequate conformal dose distributions for both serial tomography and fixed gantry IMRT.

 Figure 1-2 compares prostate treatment plans employing different numbers of fields using 3DCRT, serial tomography, and step-and-shoot IMRT. Consistent with published experience, the plan quality improves but the incremental improvement diminishes with increasing number of beams. Optimum non-uniform placement of beams can further improve dose distribution. Figure 1-5A and B shows a head and neck IMRT case for two different sets of beam angles. The patient, treated with beam configuration shown in Figure 1-5A, developed significant mucositis at the early phase of treatment. This was consistent with the “horn” in dose distribution pointed to by the arrow. Revising the beam angle arrangement as shown in Figure 1-5C led to improved dose distribution shown in Figure 1-5D.

 In general, it is most advantageous to place beams in such a way that they are maximally avoiding each other and the opposing beams with the stipulation that directions that overlap significant obstructions, such as heavily attenuating bars in the treatment couch, be avoided. For simplicity, beams often are constrained to lie in the same transverse plane. However, noncoplanar beams will provide an additional degree of freedom and potentially an additional gain in the quality of treatments. It should be noted that the beam configurations used for 3DCRT may not be optimal for IMRT.103

 Although reducing the number of beams is a desirable goal for IMRT delivered with several fixed-gantry angles and dynamic MLC, it should not be the overriding consideration. IMRT can be planned and delivered automatically in times not significantly different from the times for much simpler conventional treatments. Therefore, the delivery times for 6 to 20 beams may be quite acceptable. Keep in mind, however, that some of the current linear accelerators are limited in their ability to accurately deliver a large number of intensity-modulated beams each with a very small number of monitor units.

FIGURE 1-5. A patient with carcinoma of the base of the tongue was treated with intensity-modulated radiation therapy. (A) and (B) depict the beam angle arrangement and the resulting isodose distribution. Arrow on (C) indicates a “horn” of high dose to the left oral tongue and buccal mucosa. Rearranging the anterior beam placement (B) led to improvement of dose distribution to the normal mucosa of left anterior oral cavity (D). (Reprinted from Halperin EC, Perez CA, Brady LW. Principles and Practice of Radiation Oncology, 5th ed. Philadelphia, PA: Lippincott Williams & Wilkins, 2008:245.) Systems Using Rotating Slit (Tomotherapy) Approach

 Tomotherapy delivery has substantial differences from fixed-portal IMRT. Mackie has given an historical overview of tomotherapy, intertwined as it is with his career.48 The linear accelerator rotates during delivery, and the beam is modulated during rotation. Typically, the modulation is subdivided into small gantry angle ranges (e.g., 5 degrees), and the beam is independently modulated at each gantry angle. Each leaf is used to deliver a single rotating pencil. Pencil-beam modulation is conducted for each leaf by opening that leaf for a fraction of the gantry range consistent with the fractional fluence to be delivered from that gantry angle. For example, for a 5-degree angle range bin, if a leaf is to deliver 50% fluence, the leaf will be open for 2.5 degrees over the 5-degree range. Because of geometric constraints of modulating the radiation fan beams, only one or two thin planes can be treated with each rotation.

 The Peacock system,5 for instance, uses two banks of opposing leaves projecting to 1.7 or 3.4 cm, depending on user-selected mechanical stops. This delivers modulated beams to two abutting, independently modulated planes. The helical tomotherapy unit uses a single leaf bank with a backup collimator that allows the radiation field width to be continuously adjusted. Narrower leaf widths provide higher spatial resolution for modulation but require more treatment arcs and consequently more delivery time. The current TomoHD MLC uses tungsten leaves 10 cm thick (in beam direction), with a width of 0.625 cm. Leaves are driven pneumatically and switch in 20 msec.

3.2.3. Planning Objectives

 Optimization of ray intensities may be carried out using one of several mathematical formalisms and algorithms,70 also termed “optimization engines.” Each method has its strengths and weaknesses. The choice depends in part on the nature of the objective function and in part on individual preference. Although the details are complex, the basic principles are not difficult to comprehend. Each ray of each beam is traced from the source of radiation through the patient. Only the rays that pass through the target volume need to be traced (plus through a small margin assigned to ensure that the lateral loss of scattered radiation does not compromise the treatment). Others are set to a weight of zero.

 The patient’s 3D image is divided into voxels. The dose at every voxel in the patient’s body is calculated for an initial set of ray weights. The resulting dose distribution is used to compute the “score” of the treatment plan (i.e., the value of the objective function that mathematically states the clinical objectives of the intended treatment).

 The ray-tracing process identifies the tumor and normal tissue voxels that lie along the path of the ray. The effect of a small change in a ray weight on the score is then calculated. If the increase in ray weight would result in favorable consequences for the patient, the weight is increased, and vice versa. Mathematically speaking, the ray weight is changed by an amount proportional to the gradient of the score with respect to the ray weight. Realizing that the improvement in the plan at each point comes from rays from many beams and that each ray affects many points, only a small change in ray weight may be permitted at a time. This process is repeated for each ray. At the end of each complete cycle (an iteration), a small improvement in the treatment plan results. The new pattern of ray intensities is then used to calculate a new dose distribution and the new score of the plan, which is then used as the basis of further improvement in the next iteration. The iterative process continues until no further improvement takes place, the optimization process is assumed to have converged, and the optimum plan is assumed to have been achieved.

 Many of the current optimization systems use variations of the gradient techniques to optimize IMRT plans. These calculations are prodigious given the thousands of free parameters in variation—it was only with the advent of powerful and affordable computers that such calculations could become realizable in clinics. Direct aperture optimization has been proposed as an alternative, which reduces the parameter space and eliminates nonphysical dose distributions at the start; for a review see Broderick.105 The use of the gradient techniques assumes that there is a single extremum (a minimum or a maximum, depending on the form of the objective function). This is indeed the case for objective functions based on variance of dose and when only ray weights are optimized. For other cases, it would be necessary to determine whether multiple extrema exist and whether such multiple extrema have an impact on the quality of the solution found.

 Multiple extrema have been found to exist when beam directions are optimized or when dose–response-based objective functions are used to optimize weights of uniform beams.55,99,100 One can expect that multiple minima also exist when dose–response-based objective functions are used to optimize IMRT plans. Using simple schematic examples, it has also been shown that multiple minima exist when dose–volume-based objectives are used.106Although this may be the case in theory, the existence of multiple minima has not been found to be a serious impediment in dose–volume-based or dose–response-based optimization using the gradient techniques. In fact, in a study of dose–volume-based IMRT optimization, Wu and Mohan107 found that, starting from vastly different initial intensities, the solutions converged to nearly the same plans. The reasons for this have been speculated but not conclusively proven and need to be investigated further.

 It is possible that multiple minima do become a factor under a specific set of circumstances. If multiple minima are discovered to be a factor, then some form of a stochastic optimization technique may need to be considered. At the simplest, one may use a random search technique in conjunction with one of the gradient techniques.

 A more sophisticated stochastic technique is “simulated annealing” or its variation, the “fast simulated annealing.”10,44,55,100 These techniques allow the optimization process to escape from the local minima traps. Other forms of stochastic approaches, such as “genetic algorithms,” also have been proposed.108 In principle, the simulated annealing technique and other stochastic approaches can find the global minimum, but, practically, there is no guarantee that the absolute optimum has been found, only that the best among the solutions examined has been found. (This, of course, is true for gradient techniques as well.) Stochastic techniques tend to be extremely slow and should be used in routine work only if it is established that they are necessary. Nevertheless, some commercial systems have implemented the simulated annealing approach for IMRT optimization.5

 Also, as noted above, rapid advances in parallel processing using off-the-shelf components can dramatically reduce computation times.102 In 2005, Xu and Mueller reported an order of magnitude decrease in the time to process a CT image on a PC when equipped with a dedicated graphics board.109

3.3. Objective Functions

3.3.1. Dose-Based Objective Functions

 A simple example of an objective function is the criteria stated in terms of the sum of the squares of the differences of desired dose and computed dose at each point within each of the volume of interest. That is,

This type of objective function is called the quadratic or variance objective function. The optimization process attempts to minimize the treatment plan score SDT,0 (Equation 1-1) is the desired dose to the target volume, and Dn,0is the tolerance dose of the nth normal structure. DT,i is the computed dose in the ith voxel of the target, and Dn,j is the computed dose at the jth voxel of the nth normal structure. For normal organs, the function H (Dn,j – Dn,0) is a step function defined as follows:

In other words, so long as the dose in a normal tissue voxel does not exceed the tolerance limit, the voxel does not contribute to the score function. The quantity pn is the “relative penalty” for exceeding the tolerance dose.

3.3.2. Dose–Volume-Based Objective Functions

 Purely dose-based criteria, such as the one previously described, are not sufficient. In general, the response of the tumor and normal tissues is a function not only of radiation dose but also (to varying degrees depending on the tissue type) of the volume subjected to each level of dose. Currently, dose–volume-based objective functions are the most widely used ones clinically. Dose–volume-based objective functions are expressed in terms of the limits on the volumes of each structure that may be allowed to receive a certain dose or higher. ICRU 83 sets its IMRT reporting guidelines in terms of dose–volume criteria, and dose–volume histograms (DVHs) are a mandatory part of treatment planning.

 A practical scheme to incorporate dose–volume-based objectives has been suggested by Bortfeld et al.110 It is explained in Figure 1-6 using a simple schematic example of one organ at risk. The dose–volume constraint is specified as V(>D1) < V1. In other words, the volume receiving a dose higher than D1 should be less than V1. To implement such a constraint into the objective function, we seek another dose value D2 so that in the current dose–volume histogram V(D2) = V1. The objective function component for this OAR may then be written as

That is, only the points with dose values between D1 and D2 contribute to the score. Therefore, they are the only ones penalized.

 For the target volumes, two types of dose–volume criteria may be specified to limit both the hot and the cold spots. For instance, for the desired target dose of 80 Gy, we may specify V(>85 Gy) ≤ 5% and V(>79 Gy) ≥ 95%. In other words, the volume of the target receiving a dose higher than 85 Gy should be no more than 5%, and the volume of target receiving 79 Gy or higher should be at least 95%. Dose-based criteria can be considered as a subset of the dose–volume criteria, in which the volume is set to an extreme value (0% or 100%, as appropriate). Dose–volume criteria provide more flexibility for the optimization process and greater control over dose distributions. The reason is that dose-based optimization penalizes all the points above the dose limit, whereas the dose–volume-based optimization penalizes only the subset of points within the lower end of the range of dose values above the dose limit.

FIGURE 1-6. A method of incorporating dose–volume constraints in IMRT optimization. (Adapted from Wu Q, Mohan R. Algorithms and functionality of an intensity modulated radiation therapy optimization system. Med Phys 2002;27:701–711.)

 For the example in Figure 1-6, the dose–volume-based optimization process attempts to bring only the points between D1 and D2 into compliance with the constraint. In contrast, the dose-based optimization process attempts to constrain all the points above D1. Furthermore, dose–volume criteria are highly “degenerate” functions of dose distributions (i.e., there is a very large number of dose distributions that correspond to the same dose–volume constraint). Therefore, the optimization system has a large solution space to choose from, making it easier to find a better solution. Limitations of Dose–Volume-Based Objective Functions

 Dose–volume-based criteria have been demonstrated to have limitations. To illustrate one such limitation, consider the example in Figure 1-7 of a normal structure for which a constraint has been specified that no more that 25% of the volume is to receive 50 Gy or higher. All three dose–volume histograms shown meet this criteria. However, the DVH represented by the solid curve clearly causes the least damage. One can argue that we can overcome this limitation by specifying multiple dose–volume constraints or even the entire DVH. However, as illustrated in Figure 1-7 this would be too limiting. Multiple DVHs could lead to similar levels of injury to a particular organ, but each DVH may produce a different effect on other organs and the tumor. When this happens, DVHs usually cross each other, as shown. Only one of them is optimum so far as the tumor and other organs are concerned.

FIGURE 1-7. One limitation of the dose–volume histogram (DVH) prescription: one DVH control point (at 50 Gy and 25% of the volume) can have multiple DVH lines of inequitable clinical implications.

 To overcome the limitations of dose–volume-based criteria, they may be supplemented with biologic (or dose–response-based) criteria, for instance, in terms of such indices as tumor control probability (TCP), normal tissue complication probabilities (NTCPs), and equivalent uniform dose (EUD). Dose–response-based objective functions are the subject of ongoing investigations.55,112 At this point the ICRU includes NTCP and EUD projections in its Level 3 reporting: that is, still investigative. The report (see p. 51) notes that most of the tissue tolerance data goes back to the period before 3D imaging, but it does cite newer prospective studies involving 3DCRT or IMRT.113,114

3.4. Objective Function Parameters

 The desired IMRT dose distributions are specified in terms of parameters of the objective function. In Equation 1-1, for instance, the parameters of the objective function are the desired dose limits DT,0 and Dn,0 for target and normal structures, respectively, and the relative importance (or penalty) factors pn for deviating from the desired dose limits. Most often, the objective functions are specified in terms of one or more “soft” dose–volume constraints for each volume of interest, one for each constraint. That is, if the computed dose deviates from the desired value, the plan is not rejected, but it is assessed a penalty.

 The optimization software computes a “subscore” corresponding to each constraint. The subscore value depends on the deviation of a dose distribution from the desired dose distribution and the penalty factor. The overall score of an IMRT plan is an accumulation of subscores of individual volumes of interest. The IMRT optimization system uses the IMRT plan score to arrive at the optimum plan according to the specified objective function. The optimized solution involves trade-offs that balance specified normal tissue objectives against each other and against tumor objectives. An IMRT treatment-planning system should provide parameters that allow the treatment planner to adjust the trade-off for each critical structure in a straightforward manner. An example of this is shown in Figure 1-8, where a head and neck target volume nearly abuts the parotid gland.67 Two of six plans are shown. Plans C and F use parameters that emphasize parotid gland sparing and tumor coverage, respectively. This is an excellent example of the flexibility of moving the steep dose gradient in and out of the target volume.

FIGURE 1-8. Effect of adjusting dose-prescription parameters on the resulting treatment plan. The parotid gland and target are shown in green and blue, respectively. Plan C emphasizes parotid sparing, and plan F emphasizes tumor coverage. (From Chao KSC, Low DA, Perez CA, Purdy JA. Int J Cancer 2000;90:92–103, with permission.)

 The plan considered to be the best by the computer may not be judged the best (or even good enough) by the treatment planner. Parameters are adjusted by trial and error to obtain a satisfactory plan. A confounding factor is that a change in a parameter of one volume of interest affects not only its own subscore and DVH but also the subscores and DVHs of other structures in a complicated manner. For a complex IMRT problem in which there may be several dozen parameters, their adjustment is an extremely difficult task. The trial-and-error approach used currently is time-consuming and leads to suboptimal results. Future research based on artificial intelligence techniques may provide a systematic means of determining optimum parameter values.

3.5. Treatment Plan Evaluation

 IMRT dose distributions tend to be highly conformal but complex and unconventional. Traditional methods of evaluation and reporting may be too limited for such dose distributions. In principle, the target-dose distributions for IMRT should be more homogeneous than for 3DCRT. In practice, the opposite is the case, due in part to the competing demands of sparing of normal tissues and in part to the inadequacy of objective functions. Dose distributions in normal structures as well are, in general, more non-uniform than for 3DCRT.

 In the current practice of radiotherapy, treatment plans are evaluated using dose and dose–volume parameters including such quantities as dose to a point in the volume of interest, minimum dose, maximum dose, minimum dose to a specified fractional volume, or the volume of the structure receiving a specified dose or higher. MUs are set to deliver the prescribed dose to a specified point or to an isodose line (or surface) just enclosing the target volume. For some sites and techniques (e.g., stereotactic radiosurgery of brain tumors), an index of conformality (the ratio of the volume occupied by the prescription isodose surface and the volume of the target) is used for plan evaluation. Cumulative dose and dose–volume data are reported as a part of the patient’s chart and used for correlating with outcome.

 Because of the unconventional nature of IMRT dose distributions, especially the high degree of dose heterogeneity and fluctuations in dose as a function of position within the volume of interest, indices such as dose to a point, minimum dose, or maximum dose may not correlate well with dose–response. Instead, dose to a specified fractional volume is more appropriate, and this is the approach taken by ICRU 83. ICRU reporting now specifies a D98% or D near-min (dose to at least 98% of the PTV) and a corresponding D2% (dose received by the most heavily irradiated 2% of the PTV).3

 Helical tomotherapy (HT) versus MLC IMRT. Several dosimetric studies have compared treatment plans generated for HT or standard IMRT machines based on CT images from cancer patients. Disease sites include lung,115cervix,116 brain and craniospinal,117 and especially head and neck.118 The HT plans generally gave more homogeneous target coverage and lower doses to some OAR. However, a recent paper by Chen et al.119 reported a retrospective study on clinical outcomes in 149 head and neck patients. HT gave significantly lower doses to the contralateral spared parotid gland, but the late grade 3+ was actually slightly higher in the HT cohort (10%) as compared to the step-and-shoot IMRT group (8%). Hence, it is not clear if the dosimetric advantages seen in HT plans translate into clinical benefit to the patient.

3.6. Leaf Sequence Generation

 We used the term “leaf sequencing” to describe the general process to convert an optimized IMRT dose distribution into machine-deliverable parameters. In the case of delivery treatments using a linac-based MLC, this means the creation of the leaf position sequences as a function of MUs.

3.6.1. Fixed Intensity-Modulated Fields

 The most efficient means of delivering fixed-field IMRT is the standard MLC in dynamic mode using such methods as the “sliding-window” technique or the step-and-shoot technique. In either case, leaf position sequences as a function of MUs need to be generated. The MLC leaves are made of approximately 5- or 6-cm thick tungsten and are typically 0.5 or 1 cm wide (projected to isocenter). MLCs with leaves of a width as small as 1 mm have been introduced. Smaller leaf width may be of greater value for IMRT than for standard 3DCRT. For the former, the leaf width affects the dose delivered to the entire slice, whereas for the latter, it affects only the shape of the boundary. A smaller leaf width undoubtedly would produce more conformal dose distributions, but the electromechanical complexity and cost of the device would increase.

 Because of the smearing caused by finite-sized radiation sources, lateral secondary electron transport, and the use of multiple fields, and because of motion and positioning uncertainties, an acceptable leaf width may not need to be very small. The minimum desirable leaf width would depend on numerous factors including shapes and locations of volumes of interest, dose gradients desired, and number and orientations of beams. Although the issue of leaf width has been debated for quite some time, there are no definitive studies to guide the choice of the most suitable width.

 MLCs transmit only 0.5% to 2% of incident radiation (except through small interleaf gaps and the rounded ends of some MLCs). However, as discussed later in this chapter, because intensity-modulated treatments require substantially larger number of MUs than do the conventional uniform field treatments, the cumulative effective transmission may be considerably larger. Leaf Sequence Generation— Sliding-Window Technique

 In the sliding-window method, the gap formed by each pair of opposing leaves is swept across the target volume under computer control while the radiation is on. The gap opening and its speed are optimally adjusted. Because the dose rate of the treatment machine might fluctuate slightly, the motion is indexed to MUs rather than time. The basic principle is that as the gap slides across a point, the radiation received by the point is proportional to the number of MUs delivered during the time the tip of the leading leaf goes past the point and exposes it until the tip of the trailing leaf moves in to block it again. (The point also receives additional radiation transmitted through or scattered from the leaves, which must be accounted for. See later discussion in this chapter.)

 The setting of the gap opening and its speed for each pair at any instant are determined by a technique first introduced by Convery and Rosenbloom120 and refined and studied further by Bortfeld et al.,22Spirou and Chui,38 Spirou et al.,39 Stein et al.,40 Svensson et al.,121 and others.42,75 Knowledge of the maximum leaf speed is taken advantage of to maximize the gap between the opposing pairs of leaves and, therefore, to minimize the treatment time. The number of leaves participating in the delivery of a beam depends on the projected size of the target volume. The data describing leaf trajectories, produced by the leaf sequence-generation process, are in the form of a table of positions of leaves versus the corresponding MUs. Leaf Sequence Generation— Step-and-Shoot and Multisegment Techniques

 For the step-and-shoot technique (as well as for the multisegment technique) the fixed-gantry radiation beam is composed of multiple static MLC segments, with each segment having its own aperture shape and weight or monitor (MU) settings. The leaf sequence-generation algorithms take the optimized intensity pattern as the input and decompose it into multiple segments, each to be shaped as an aperture formed by the MLC. Fluence intensity throughout each MLC segment is relatively uniform. The summation of all static segments yields the required intensity-modulated dose distributions. Ideally, the segments are sorted to minimize the MLC leaf travel time between the segments. Note that such sorting is neither necessary nor possible for the sliding-window technique.

 The first step of the leaf sequence-generation process is the discretization of the continuous intensity distribution into a limited number of intensity levels. These intensity levels are then converted into leaf sequences using one of several methods described in the literature. Bortfeld et al.,22 for example, proposed a method in which each row of intensity is handled separately, similar to the sliding-window algorithm. The advantage is that the total number of MUs is small but at the cost of a possibly large number of segments. Xia and Verhey122 proposed the so-called areal algorithm. Instead of dividing the intensities into levels of equal steps, they divided them into levels in powers of 2 to reduce the number of steps and to gain efficiency. Wu et al.112 proposed a technique called the “k-means clustering” in which the intensity levels are grouped together based on their values and the user-specified error tolerance levels. The intensity levels are not equally spaced and can be arbitrary.

 Unlike in the sliding-window algorithm, the maximum leaf speed is not important for the step-and-shoot and multisegment techniques. Similarly, while the number of segments is not an issue for the sliding-window techniques, it could affect the step-and-shoot delivery efficiency significantly. For the former, the only penalty of the large number of segments is the size of computer storage, whereas for the latter it leads to inefficiency because the beam is off during the transition between segments. Furthermore, for some linear accelerators, there is an overhead time associated with each segment.

 Que123 compared several step-and-shoot algorithms and found that the algorithm used by Xia and Verhey122 frequently, but not always, produced the lowest number of segments. Other investigators have reported methods to minimize the number of segments as well. The algorithm of Dai and Zhu124 checks numerous candidates for each segment, and the candidate that would result in a residual intensity matrix with the minimum complexity is selected. If more than one candidate exist with the same complexity, the one with the largest size is chosen. Langer et al.125 reported a technique based on integer programming that can minimize the number of segments under the constraint that the MUs do not exceed a certain limit. It was found that this technique produces considerably fewer segments than produced by the algorithms of Bortfeld et al.10,110 and Xia and Verhey122 for the same or fewer MUs.

3.6.2. Monitor Units of Intensity-Modulated Radiation Therapy Beams

 Based on methods similar to those previously described, software systems have been developed to convert intensity distributions to leaf trajectories. The input to this software is the intensity distribution for each field in terms of MUs or, to be more precise, “effective” MUs. Effective MUs are the fractions of MUs transmitted through the intensity-modulation or compensation device. The intensity distribution-to-leaf trajectory conversion software not only produces trajectories but also computes actual MU settings for each beam as a natural byproduct of the conversion process. Trajectories of leaves and the MUs for each beam are transmitted to the computer-controlled radiation treatment machine for dosimetric verification and delivery of treatment.

 It is important to note that the relationship between the prescribed dose and the MUs required for delivering each of the intensity-modulated beams is highly complex and not obvious. There is no practical way to calculate MUs by hand as is done for traditional treatments as an independent check of the predicted MU values. To ensure patient safety and to satisfy the requirements of the independent check, some systems have implemented independent software for a second MU calculation. Others have adopted the policy to measure the dose or dose distribution for each of the beams before the first treatment.

3.6.3. Impact of Multileaf Collimator Characteristics

 ICRU 83 notes that the tolerances for MLC operation must be more stringent than even those required for beam blockage in 3DCRT. This stems from the steep dose gradients made possible by and employed with IMRT. Slippage of leaf position would cause a cumulative degradation of the dose distribution actually delivered. Leakage through closed leaves may also pose a problem for which consideration must be given while planning.126 Adjustments to leaf trajectories are required to account for the various effects associated with MLC characteristics, including the rounded leaf tips, tongue-and-groove leaf design, interleaf and intraleaf transmission, leaf scatter, and collimator scatter upstream from the MLC. The accuracy of dose delivered and the agreement between calculated and measured dose distributions depend on the adequate accounting of these effects. Approximate empirical corrections are applied for these effects by the algorithms and software that convert optimized intensity distributions into leaf trajectories.

 MLCs have an interlocking tongue-and-groove leaf design to minimize interleaf leakage. However, there is a difference between interleaf leakage and leakage through the leaves. This difference can become significant for beams that require large number of MUs and in portions of the beams that receive large fractions of their dose through leakage. Currently, this effect is ignored, although the use of Monte Carlo techniques to account for it is being investigated.127,128

 In addition, there are circumstances during creation of intensity profiles when a thin strip of the irradiated medium is shielded by the tongue of one leaf pair or the groove of the adjacent leaf pair rather than being completely exposed or completely blocked. van Santvoort and Heijmen41 demonstrated that this leads to an underdosage in the thin strip. They, and subsequently Webb et al.,46 also showed that this effect could be removed by the use of leaf motion-synchronizing techniques. However, such techniques result in an increase in the number of MUs. Furthermore, this effect is not considered to be of significant clinical consequence because of the smearing caused by multiple fields and the positioning and motion uncertainties. Using different collimator angles for each field can reduce this effect further.

 Depending on the complexity (the frequency and amplitudes of peaks and valleys) of the intensity pattern, points within the field aperture may receive a substantial portion of the dose as a result of radiation transmitted through or scattered from the leaves when the points are in the shadow of the leaves. Points outside the leaf aperture receive their entire dose through these “indirect” sources.

 The complexity of intensity distributions produced by the IMRT optimization process depends on a combination of several clinical factors including the shapes, sizes, and relative locations of tumor and normal tissues; required tumor dose; dose homogeneity; and dose–volume limits of normal tissues. Intensity distributions for head and neck cases, for example, tend to be considerably more complex than for prostate cases.

 For beams with highly complex intensity patterns, the average window width to deliver the treatment tends to be small and, for the same dose received by the tumor, the treatment time (i.e., the number of MUs) is long. Consequently, the contribution of radiation transmitted through and scattered from the leaves may form a significant fraction of the total dose delivered. Because these contributions are accounted for approximately, the uncertainty in dose delivered is increased. In addition, the differences between interleaf and intraleaf transmissions may no longer be negligible. Another consequence of complex intensity patterns is that the lower limit of the deliverable intensity is high.

 The deliverable dose distributions may be significantly different from the original optimized ones. There are different ways to overcome the difficulties resulting from the differences in desired and deliverable dose distributions. For example, if the deliverable dose to a particular normal structure is higher than the original optimized dose, the planner could modify the objective function to demand an appropriately lower dose. Alternatively, the optimization loop could include a pass through leaf sequence generation and calculation of deliverable dose distributions. The optimizer then adjusts ray weights based on deliverable dose distributions rather than the idealized ones. This scheme has been investigated by Siebers et al.76


4.1. The Importance of Intensity-Modulated Radiation Therapy Quality Assurance

 A number of QA steps unique to IMRT are needed to ensure the accuracy and safety of treatments. These include QA of the MLC in dynamic mode, dosimetric verification of each dynamic beam as well as of the composite treatment plans, portal imaging, treatment verification, in vivo dosimetry, and reduction in uncertainty associated with daily positioning and internal organ motion during irradiation. In recognition of the special demands of IMRT, the AAPM recently commissioned Task Group 142 to recommend new QA guidelines and these have been published.129 The updated “Table II” recommendations for monthly QA from Task Group 142 are reproduced here as Table 1-1. Both physical and clinical quality assurance concerns are also addressed in the appendices to ICRU 83.

 When using conventional 3DCRT, MLC leaf position calibration errors influence the accuracy of the radiation distribution at the portal boundary. Because of PTV and beam penumbra margins, small errors in leaf calibration will have a minimal effect on the target volume dose. The accepted leaf calibration accuracy is 2 mm but this is too large3 since in IMRT the MLCs are used to generate inhomogeneous fluence distributions.

 In the sliding-window technique, for instance, this is done by adjusting the velocity and width of leaf gaps during radiation delivery. If the MLC calibration is inaccurate, the delivered dose distribution will be in error. The error is a function of the ratio of leaf calibration error to the sliding-window width. For example, a 1-mm imprecision in the gap would result in a 10% error in dose if a uniform field were to be delivered using a sliding window of 1 cm. For step-and-shoot delivery, magnitudes of dose errors are greater (owing to the steep dose gradients near the MLC leaf edges), but they are confined to the subfield edges. Thus, it is important that the manufacturers of MLCs used for IMRT ensure that the leaves can be positioned with an accuracy of better than 0.25 mm, and the physicists must ensure through routine QA procedures that such precise positioning is achieved and maintained.

 It is interesting that integral dose error is similar for both the step-and-shoot and sliding-window techniques, but the distribution of the error is different.

 Because MLC leaf calibration and the accuracy of MLC operations influence the delivered dose distribution, new, more rigorous MLC QA procedures have been developed. Chui et al.,66 LoSasso and Chui,130 and Ling et al.,7among others, have developed QA procedures specifically for MLCs used in dynamic mode. Periodic QA checks must ensure that the leaves of the MLC do indeed move to their designated positions at the specified values of MUs. Moreover, to ensure safe and accurate delivery of treatments with an MLC, the manufacturers must include redundant and independent sensors for the leaves of the MLC. Furthermore, in the event of treatment field interruption and resumption, there should be no perceptible change in dose delivered.

 Another aspect of QA important for IMRT is the daily positioning uncertainty and motion during irradiation. IMRT is a highly conformal and highly precise form of radiotherapy frequently used to escalate dose. Dose distributions may have steep dose gradients between the target and the neighboring normal structures. Furthermore, margins may be much smaller than in conventional treatments. Patient positioning and immobilization requirements are more stringent than ever to ensure that the target volumes are covered adequately and the normal tissues are spared adequately. In fact, special immobilization devices and techniques are being developed to reproducibly and accurately position the target volume and normal anatomy. Many of these devices already are available commercially (e.g., rectal inserts to improve positioning for prostate IMRT).

 Similarly, motion during treatment, mainly as a consequence of respiration, also can be a serious problem for IMRT of sites in the thorax and abdomen. Because IMRT is delivered dynamically, the moving target volume may move in and out of the instantaneous field of radiation. Some portions of the target volume may get more than the planned dose, whereas others may get less. A way to minimize the effects of respiratory motion would be to use “gated treatments” in which radiation and leaf motion are turned on only during a specific, reproducible portion of the respiratory cycle or in an interval during which the patient’s breath is voluntarily, or involuntarily, held.131 New methodologies, typically employing CT imaging are being used to synchronize patient’s breathing motion with the irradiation beam.132134

4.2. Intensity-Modulated Radiation Therapy Dosimetry and Measurement Equipment

 A full discussion of the equipment and methods (e.g., solid state arrays and ionization chambers) used in dosimetry is outside the scope of this book and can be found in standard texts on medical physics and radiation oncology; the reader is referred to chapters 6, 7 and 8 of the Principles and Practice of Radiation Oncology.135

4.3. Overview of Quality Assurance Process

In the implementation of a new treatment technology into routine clinical use, there are usually three distinct but closely related phases:

 Acceptance tests: This is the initial set of tests to ensure that the hardware and software meet the factory or customer-provided specifications. Usually, but not always, the written specifications contain the necessary instructions or guidelines for these tests (in order to avoid legal ambiguity in the measurements). It is also a good opportunity for the users to establish some performance baselines, especially for the hardware purchased.

 Commissioning tests: The IMRT commissioning is a process to implement IMRT treatments using the customer’s hardware and beam data. Various groups have studied the general guidelines for commissioning a treatment planning system, and the AAPM issued a new report on IMRT commissioning in 2009.136 The process usually starts with collection of essential beam data for beam modeling. The parameters of the dose-calculation algorithm are then tuned to provide the best performance for the user’s beam. Additional tests should be performed to evaluate the limitations of the treatment-planning system and a solution or a work-around should be found if the problem is clearly identified. Then IMRT phantom measurements should be performed to test the accuracy of the delivery system and data connectivity. If the accuracy is judged to be acceptable, the system can be released to the clinic after the necessary user training and procedural implementations. It is recommended that a small (interdisciplinary) focus group should be assigned to lead the IMRT implementation in the clinic. The “train-the-trainer” approach has proven to be effective in translating new technology into routine clinical practice.

 On-going QA: After the system is released to the clinic, it is important to establish a routine QA program. The performance of the various steps involved in IMRT treatments needs to be tracked so that the quality of the treatments can be maintained.

4.4. Patient-Specific and Equipment Quality Assurance

 Because of the complexity of irregular field shapes, small-field dosimetry, and time-dependent deliverable leaf sequences, it is recommended by the American Association of Physicists in Medicine and ASTRO that patient-specific QA should be performed as a part of the IMRT management process and as a requirement for billing for IMRT services. Figure 1-9 shows the general categories of patient- and equipment-specific QA.

 Patient setup, although not specific to IMRT dosimetry, is considered a key step in ensuring accurate IMRT treatments. A variety of image-guided localization techniques have been proposed for use with IMRT treatments, from simple orthogonal portal films to the beam’s eye view portal film with IMRT intensity pattern overlays, imaging of implanted fiducials,138,139 daily ultrasound-guided localization,140142 and to the most integrated tomotherapy solutions143; see also Section 5 of this chapter. A somewhat related problem of organ motion due to breathing has been discussed earlier. Several recent studies have examined the use of cone-beam CT for patient setup or respiratory gating.144147 A detailed discussion of these specific image-guided procedures is beyond the scope of this chapter, but QA in patient positioning remains an important issue for IMRT.

FIGURE 1-9. Separation of IMRT quality assurance (QA) into patient-specific and equipment-specific QA.

• The implementation of patient-specific QA depends highly on the specific institution. For example, dosimetric measurements of MU settings can be verified for each beam individually (usually in a flat [slab] phantom geometry) or for the composite treatment plan (usually in a specially designed phantom, but it is also possible to use the simple slab phantom setup). Unlike single-beam verification in which the single-beam dose distribution can be significantly different from that in the original patient plan, the advantage of measuring the composite treatment plan in a phantom (regardless of the shape of the phantom) is that the composite dose distribution or the dose “pattern” generated in a phantom is usually similar to those in the original patient plan. This can be useful in selecting the measurement points or in visualizing potential dose errors. Absolute dosimetry is usually referred to as “MU verification” for IMRT. The traditional manual process for MU verification is virtually impossible to perform because of the large number of fields involved and the irregular shapes and sizes of the treatment segments. Attempts have been made to verify MU settings in an IMRT plan using alternative calculation methods.148However, these alternative calculation methods cannot predict the uncertainties during the actual delivery at the treatment machines and are also subject to limitations and approximations in their dose-calculation models.

• The most reliable and practical technique currently for IMRT MU verification is still the ion chamber-based point dose measurement in a phantom. Absolute dose measurement in a phantom is usually performed through a process called the “hybrid phantom plan.” In this plan, all beam angles and deliverable intensity patterns for a patient plan are transferred to the phantom, and doses in the phantom are computed for QA. The basic assumption in this process is that if the dose calculated in the phantom agrees with the measurement in the phantom, then the dose delivered to the patient agrees with the dose calculated in the patient.

• Relative dosimetry is usually performed using radiographic films or two-dimensional (2D) array detectors. The process is similar to absolute dose measurement using the hybrid phantom plan technique. For film dosimetry, it is important to convert film density into relative dose using a film-calibration process. Because of the additional dimensionality, it becomes difficult to define good numerical criteria for evaluating relative 2D/3D measurements. Various numerical indicators (such as the distance to agreement, gamma index, or normalized agreement test index) have been proposed. In particular, the concept of gamma, combining the dose difference and distance to agreement, is appealing in evaluating 2D or 3D dose distributions.

• For clinical applications, the most reliable and practical way to evaluate a 2D distribution is to overlay the measurement isodose lines with the calculated ones. Special attention should be paid to the low-dose regions near critical structures in the original patient plan. Attention should also be paid to the systematic shifts of isodose lines, which may reveal whether the isocenter or any reference setup point is offset. The relative dosimetry verification for IMRT should be performed in conjunction with the absolute dose verification for IMRT. It would be useful if the relative dose distribution can be normalized to the absolute dose measurement point, which converts the relative dose measurement into absolute dose distributions.

• Two-dimensional fluence verification of intensity patterns gained popularity with the invention of 2D array detectors and the necessary software.148150 Fluence verification usually is performed for each IMRT beam at a fixed gantry angle with or without a flat phantom geometry. The purpose of fluence verification is to make sure that the intensity patterns created in each IMRT plan can be faithfully delivered under ideal conditions (2D, beam’s eye view). Fluence verification should be combined with other patient-specific and equipment-specific QAs to ensure that IMRT treatments are executed accurately.

• Figure 1-9 also illustrates equipment-specific QA procedures. In general, IMRT QA is a subset of general equipment QA processes. The technology of IMRT and techniques for QA are also evolving. It is strongly suggested that users of IMRT should attempt to attend national meetings and technology conferences or training courses so that their knowledge about the use of IMRT can be updated regularly.

• IMRT has been variously qualified as opaque, unintuitive, and nontransparent, partly because it is delivered using dynamic techniques. Many are skeptical about whether the dose distribution displayed on an IMRT plan is, in fact, delivered. Furthermore, because of the complexity of the computations involved, there is no practical way to verify the MU settings by hand calculations as is done for conventional treatments. Moreover, because of the inherent non-uniformity of IMRT fields, it is important to know the dose accurately at every point within the beam. One way to check if the intended dose would be delivered to the patient at the time of the treatment is to conduct dosimetric verification measurements.

• Two broad categories of IMRT treatment plan verification approaches have been developed for MLC-based IMRT. First, the dose distribution from radiation fields are independently measured and evaluated. This often is accomplished by using a flat homogeneous water-equivalent phantom and irradiating each field independently. The film-measured dose distributions are compared with calculations conducted by the treatment-planning system under the same geometric conditions. For calculation of dose distributions, each field is transferred to a treatment plan with a flat homogeneous phantom. This technique has the advantage that discrepancies between the planned and delivered dose can be attributed to individual radiation portals. However, the total integrated dose distribution is not checked.

• The second method uses a phantom that is irradiated by all beam portals, allowing the evaluation of the total dose distribution delivered.151,152 Typically, ionization chambers and radiographic film are the dosimeters used for these measurements. Although ionization chambers can be benchmark-quality dosimeters, they suffer from volume averaging137 and are inefficient for measuring multiple points. Because of the complexity of the dose distributions being measured, a 2D dosimeter is required for a thorough evaluation of non-uniform dose distributions. Quantitative radiographic film measurements require careful dose calibration using independently measured sensitometric curves. The film optical densities are measured and converted to absolute dose using film-calibration data and are compared with the predictions of the treatment-planning system.42

• In vivo dosimetry is commonly used to verify the dose delivered by conformal therapy radiation fields. The complex fluence distribution of IMRT fields makes quantitative use of in vivo dosimetry, specifically the use of skin-surface mounted dosimeters, difficult.

• Film, thermoluminescent dosimeters, and diodes may not be sufficiently accurate and are laborious to use.In addition, thermoluminescent dosimeters and diodes are incapable of providing detailed information. In the long run, the most efficient way to verify fixed intensity-modulated fields is expected to be with real-time 2D dosimetry systems using appropriately calibrated electronic portal imaging devices. A general review of electronic portal imaging devices (EPID) has appeared recently.153 Such devices could be used for dosimetric verification of IMRT beams before treatment delivery and for exit dosimetry using transmitted portal dose images (PDIs). For electronic portal imaging devices to be used for pretreatment dosimetric verification and exit dosimetry, they must operate in the integration mode to capture the transmitted radiation over the entire exposure of each beam. The result is a PDI that can be compared with an intensity-modulated digitally reconstructed PDI.

• For pretreatment dosimetric verification of a given beam, a PDI may be created using a 3D-treatment-planning system to compute the dose deposited in the electronic portal imaging device detector. For exit dosimetry, the PDI may be calculated using the 3D CT image of the patient. In either case, for accurate dosimetric verification, the effect of scattered radiation and the variation in response of the detector with energy must be included. The former effect can be taken into account with dose-spread kernel superposition methods, but both can be accounted for using Monte Carlo techniques.


• All current cancer radiotherapy is image-guided in the sense that some picture of the tumor region (most often CT) is used to plan the course of treatment whether by brachytherapy or external beam irradiation. However, the new image-guided radiation therapy (IGRT) methods coming onstream are using dynamic and/or functional imaging of the tumor. See Mell et al.154 for an overall summary of IGRT. See also recent reviews by Allison et al.155 on IGRT and van Elmpt153 on portal imaging.

• Standard RT is static in that a single treatment plan is developed by the radiation oncologist and medical physicist. Fiducial markers, either external (e.g., moles, tattoos) or internal (gold pins), may be used to register the patient on the couch prior to each treatment session. For sites such as thoracic, where respiratory motion is a significant factor, treatment margins are increased to adequately cover the tumor through the inspiration–exhalation cycle.

5.1. Interfraction Adjustments

• These treatment alterations are concerned with changes in the target location and size that occur on a timescale much longer than that of breathing motions. Many patients are treated with combined modalities, for example, chemotherapy and RT. Therefore one may see (or anticipate) significant movements of the tumor due to cell kill, anatomical changes in the body (e.g., weight loss), and long-term shifts in the placement of the parent organ as tumor burden is lowered.

• Hence, there are significant sources of systemic error in a regimen of RT with a dozen or more treatment sessions spread over several weeks. When employing large RT fields, the errors introduced would have been negligible since the entire volume of the patient’s body around the tumor was included in the field. However with 3DCRT and more so with IMRT, dosage gradients can be large and positioning errors on the order of even 1 cm or less can mean unintended undertreatment of the tumor and greatly increased normal tissue toxicity.

• The first source is setup errors: changes in position of the patient on the treatment couch which differ from the geometry indicated in the planning CT scans used to design the radiation beam angles and intensities. Fiducial markers can assist but even these can be in error since they assume an unvarying relation of the marker position to the internal location of the malignancy. Simple positioning errors can be corrected by linear translations of the couch relative to the gantry based on the fiducial markers.

• The second source of error is changes that occur in tumor size and location during treatment. If a tumor shrinks, normal tissue will occupy the corresponding volume. To detect such changes, new images would be needed. Ideally these would be high-resolution CT scans, as can be obtained with linac machines with a movable CT scanner (the “CT on rails”). Simpler kV cone-beam CT (CBCT) scanners have also been incorporated on the treatment linac, with their beam axis orthogonal to the megavoltage (MV) photon beams. These cameras provide scans with sufficient resolution for patient repositioning or even alternation of the treatment plan itself. See Boda-Heggemann et al.156 for a review of kV CBCT. Helical tomography machines use the MV rays both for imaging and for treatment. For example, Sheng et al.157 retrospectively studied 10 patients with nasal cavity or nasopharyngeal cancer treated with helical tomotherapy in 25–33 fractions; daily MVCT images were taken. They reported [that the mean setup error was reduced from 3.6 to 1.7 mm with weekly image guidance; in addition, equivalent uniform dosage to OAR was lowered from 1.8 Gy to 0.8 Gy. Fixed-gantry RT machines can be used for MV-CBCT by employing 2D detectors, rather than the 1D detector arrays used in conventional CT.158 Although kV CT has higher resolution, MV x-rays are much less sensitive to metallic artifacts (e.g., dental fillings or surgical implants). A graphic example was provided by Hong et al.159 who provided MV images of an automotive carburetor, images impossible to generate with kV x-rays.

• Another concern with the sharp dosage boundaries available with IMRT is the precise definition of the tumor volume. Most treatment planning uses anatomical information from CT scans, but there may be malignant tissues not clearly visible on CT—hence the rapidly expanding use of biologically based imaging modalities.

• Positron emission tomography (PET) can be used to image biologically active tumor sites. Advantages are relatively low radiation dose (comparable to a pelvic CT) and specificity to metabolically active tumors, especially in soft tissues, that have poor contrast on CT or ultrasound (US). Disadvantages are high cost, short half-life of the positron emitting nuclides, limited spatial resolution, and complications of image registration. See Chapter 2 on the use of PET for image-guided RT.

• The vast bulk of PET imaging uses 2-[18F]-fluoro-2-deoxyglucose (FDG). For a recent review of PET tumor delineation see Cheebsumon et al.61 This analog is taken up by metabolically active cells and accumulated since it cannot be degraded by the cell’s glycolytic enzymes. Since tumor cells are growing, they preferentially take up the FDG and can be imaged by PET.

• Another biologic target for tumor imaging is hypoxia. This is particularly important for RT since hypoxic cells are more resistant to radiation damage. Imam160 has reviewed the use of new PET tracers for hypoxia, particularly 18F-fluoromisonidazole (18F-MISO). Another candidate is 60Cu-diacetyl-2,3-bis(N(4)-methyl-3- thiosemicarbazone) (Cu-ATSM). This agent was used by Chao et al.161 to guide IMRT treatment of head and neck cancer.

• Ultrasound (US). This is a relatively inexpensive, widely available imaging method, which images anatomical features based on differences in the local mass density. Image registration is still a task, but unlike CT or PET, no ionizing radiation is employed. Given the speed of sound in typical biologic tissues (about 1,500 m per second) and US transducers operating at or above 2 MHz, the spatial resolution should be better than 1 mm. A key advantage is the small size of the transducers, which allows presence in the linac treatment area. Potential pitfalls are improper probe-to-skin placement, leading to deterioration of image quality and reproducibility and difficulty in resolving structures with similar mass density (e.g., soft tissues). Also, the pressure applied to the probe by the operator to maintain good acoustic coupling may deform nearby soft tissue which could lead to errors in targeting.

• Magnetic resonance imaging can give high-resolution images without using ionizing radiation. Also, by employing specific RF pulse sequences MRI can contrast-select for certain tissue types (e.g., CNS) or even malignant versus nonmalignant regions due to known variations in the spin lattice (T1) or spin–spin (T2) relaxation times. In particular, some brain malignancies are superbly detected by fluid attenuated inversion recovery MRI (FLAIR sequences) but poorly imaged by CT and standard MRI. MR methods can be used to image both anatomical structures and molecular targets, and these exciting applications to oncology have been reviewed by Bradbury and Hricak.57

• MRI is still relatively expensive as compared to CT and requires a high-field open-bore magnet, typically superconducting with cryogenic cooling. This makes placement in the linac treatment area rather difficult. However, the technology is rapidly changing, and hybrid PET–MRI systems are being built.162

• CT. Various methods have been used to get co-registered x-ray pictures while the patient is on the RT treatment couch as noted above. Some new machines are using x-rays from the treatment beam itself to generate images. Gayou, Miften, and co-workers have described the commissioning and implementation of megavolt cone-beam CT in IMRT planning.163,164 Specific applications of MV-CBCT in prostate cancer treatment have been reported by Zucca et al.165 In all cases, the CT scan is used to readjust the treatment, in many cases using dynamic multileaf collimators (DMLC).

5.2. Intrafraction Alternations

• The most serious tumor motions on the timescale of seconds are due to the respiratory motions of the patient. Efforts have been made to coach patients to control breathing during irradiation or to restrict it with external restraints (and oxygen masks), but some of these are not options for patients with poor pulmonary function. The goal is to track (or estimate) the tumor movement in real time and interactively modulate the treatment beam accordingly.

• The most common imaging method being used now is called 4D gated imaging: either 4DCT or 4D PET (or both in combination), that is, images formed in three spatial dimensions with the fourth being time. The idea is that for both CT and PET, one can gate data acquisition into separate time windows, depending upon where one is in the respiratory cycle. (Similarly, the treatment beam can be gated in synchrony with breathing motions.)

• IMRT for lung cancer requires some method to account for tumor and organ motion during treatment planning and delivery; these techniques include both respiratory gating132 and 4DCT planning.166Starkschall et al.167 recently reported direct 4DCT measurements of interfraction GTV movement during free breathing and concluded that breath-hold gating provides reproducible tumor localization. With CBCT or orthovoltage x-ray, the appropriate margins are 0.3 cm for implanted fiducials and 0.8 cm for bony landmarks.

• Determination of the actual respiratory cycle can be done externally (infrared reflectors on the patient’s chest or abdomen) or internally from the images themselves. Jiang168 has reviewed respiratory gating. In the simplest case, one waits for the extremum of exhalation for high-dose irradiation with tight margins. Irradiation throughout the breathing cycle requires rather sophisticated computer control of the treatment beam using DMLC. This in turn requires strict redundant shutoff mechanisms to prevent overdosage or misdosage caused by a software problem.


• When high-energy charged particles, protons or carbon ions, for example, move through matter, these ionized atoms deposit their kinetic energy along their path. At the end of their path, immediately before the particles come to rest, they release the maximum amount of energy before rapidly dropping to the zero-energy state. This is called Bragg peak, after William Henry Bragg who reported it in 1904.

• In order to cover the location and depth of the tumor within the irradiated tissue, a technique named the spread-out Bragg peak or SOBP may be employed. Figure 1-10 illustrates the dose penetration for MV photons as compared to monoenergetic protons at various beam energies, using water as a target. Notice the sharp spike for each proton beam at the end of its range. By allowing a range of proton energies, one can obtain quite an isotropic beam profile over a targeted depth range.169,170 Note also that the normal tissue in front of the proton SOBP experiences a much lower radiation dose as compared with those in front of photons, wherein the beam intensity falls monotonically with depth. Hence the radiobiologic effects of proton beams (and other heavy ions) are distinctly different from those of photon irradiation. For a concise review, see Dorr and Joiner.171

FIGURE 1-10. Dose penetration of MV photons and protons in water. The left broad curve is a simple-model-based exponential attenuation of the incident x-rays, convoluted with a shorter exponential fall off in energy deposition due to forward scattered electrons. Here the forward scatter was given a characteristic length (1/e) of 0.5 cm, and the attenuation is 10 cm. This curve is similar to the attenuation data curve in figure 2 of Ahuja et al. [Med Phys 1980;7(5):537–544]. The spiked curves on the right are theoretical spectra for monoenergetic protons based on the model in Bortfeld [Med Phys 1997;24(12):2024–2033]. The curves exhibit the “Bragg peak” caused by the fact that charged particles lose their energy to the surrounding medium mostly at the end of their trajectory. Note that the depth of penetration is dependent on the initial energy of the proton. By the use of attenuating filters one can create a distribution of incident particle energies and achieve a spread-out Bragg peak with a reasonably isotropic dosage within a range of depth. The figure also shows the dosage of the sum of monoenergetic proton beams of 150 to 190 MeV in 10 MeV steps. With even minimal bandpass filtering, the spikes can be smoothed. Clearly a beam containing all energies within a window could be made essentially flat in the region of the Bragg peaks. (Reprinted from Chao KSC, Perez CA, Brady LW. Radiation Oncology Management Decisions, 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins, 2011:52.)

• At the end of 2012, there were a total of 41 proton/heavy ion therapy centers in Canada, China, England, France, Germany, Italy, Japan, Korea, Russia, South Africa, Sweden, Switzerland, and United States; and over 88,400 patients had been treated (PTCOG,

• Indication for particle therapy can be categorized into two clinical scenarios:

º To escalate tumor dose while maintaining normal tissue exposure at a similar level to that of photon therapy. The target tumors include (but are not limited to) uveal melanoma (ocular tumors), skull base and paraspinal tumors (chondrosarcoma and chordoma), and unresectable sarcomas. In all these cases, proton therapy achieves significant improvements in the probability of local control over conventional radiotherapy.172174 Charged particle therapy for ocular tumor (uveal melanoma) requires only a low-energy (about 70 MeV) proton beam.

º To reduce short- and long-term side effects by limiting the dose to normal tissue. In this case, tumor dose will remain the same so that there is no increase in tumor control. The target tumors include but are not limited to pediatric neoplasms (such as medulloblastoma) and prostate cancer.

• In the case of pediatric cancers, there is convincing clinical data showing the advantage of sparing developing organs by using protons, and the resulting reduction of long-term damage to the surviving child.

• Dose distributions for IMRT and proton beam therapy have been reviewed by Palm and Johansson.175 They concluded that while proton beam therapy showed little advantage over IMRT in dose delivery to the target volume, there was a clear decrease in average dose to OAR with protons as compared to standard RT or IMRT.

• Proton therapy for head and neck cancers has been reviewed by Chan and Liebsch.176 More recently, Ramaekers et al.177 compared IMRT, protons, and carbon ions in treatment of head and neck disease.

• Recent studies comparing intensity-modulated proton therapy to IMRT include the following:

º Breast: Ares et al.178 performed a dosimetric comparison on 20 left-sided breast cancer patients using 3DCRT, IMRT, or protons for postoperative RT. 3DCRT plans had excessive left lung V20; proton therapy had significantly lower OAR dosage as compared to IMRT (left lung and cardiac V5 were smaller by more than 2.5 times; cardiac V22.5 was reduced by a factor of 20).

º Pancreas: Bourchard et al.179 took a 3-cm T4N0M0 pancreatic neck tumor from a CT data set and virtually moved the malignancy laterally in 5-mm steps, and then generated treatment plans with dose escalated to 70 Gy with 3DCRT, IMRT, and protons. 3DCRT gave inadequate target coverage; IMRT was more conformal except where the small bowel was nearby. The choice between IMRT and protons thus depended on the tumor position.

º Prostate: IMRT, IMRT plus proton boost, and proton RT alone plans were created for five high-risk prostate patients.180 Protons showed a moderately lower rectum and rectal wall irradiation. They noted that the low-dose “bath” seen with IMRT was absent in the proton plans, but the doses to the femoral heads were higher with protons. Nguyen et al.181 and Sheets et al.190 have reviewed 3DCRT, IMRT and protons.

º Liver: Petersen and co-workers182 compared proton therapy to SBRT on 10 patients with unresectable liver tumors. The spared liver volume was lower with protons in all 10 patients at the highest prescription dose: 1411 versus 955 cm3. All plans achieved the constraint that V>15 Gy should be less than 700 cm3.

º Head and neck: Ten patients with advanced oropharyngeal cancer had plans drawn for 3DCRT, IMRT, and proton therapy by van de Water et al.183 In all cases, protons gave better dose conformity; 3DCRT was clearly worse. Mean dose to the parotid was 50.8 Gy for 3DCRT, 25.5 Gy for IMRT, and 16.8 Gy for protons. In an earlier study by Widesott et al.184 six patients were treated with helical tomotherapy (HT) (with boost) of nasopharyngeal cancer, and were then replanned for proton therapy.185 Mean parotid dose was slightly less with protons (5.6 vs 6.4 Gy), but V20 Gy and V30 Gy were smaller for HT, as were the total body volumes receiving 30, 20, and 10 Gy (14.5%, 19.4%, and 23.1%, respectively). Luo et al.186 found reductions of target dose heterogeneity of up to 56% in IMPT (intensity-modulated proton therapy) versus IMRT in Monte Carlo simulations for the Fox Chase laser-accelerated proton accelerator. Kosaki et al.187 compared treatment plans for skull base meningioma and found better OAR sparing with protons (and also with carbon ions) as compared to IMRT.

º Esophagus: IMPT and IMRT dosimetric plans were compared for 10 patients with unresectable distal disease by Welsh et al.188 They reported a significant reduction both in mean lung dose (3.18 vs 8.27 Gy, P < 0.0001) and in the lung volumes receiving 5, 10, and 20 Gy.

º Pediatric bladder/prostate rhabdomyosarcoma: Seven patients were treated and followed up for a median of 27 months; all received concurrent chemotherapy, and four had radiation following gross total resection.189 Dosimetric comparisons showed that proton therapy gave improved mean dose sparing for bladder, testes, growth plate, femoral head, and pelvic bones.


1. Thomson W. On the secular cooling of the Earth. Trans Roy Soc Edinburgh 1864;23:167–169.

2. Cooley JW, Tukey JW. An algorithm for machine calculation of complex Fourier series. Math Comput 1965;19(90):297–301.

3. ICRU. ICRU Report 83: prescribing, recording, and reporting photon-beam intensity-modulated radiation therapy (IMRT). J ICRU 2010;10(1):1–106.

4. Bernier J, Hall EJ, Giaccia A. Radiation oncology: a century of achievements. Nat Rev Cancer 2004;4(9):737–747.

5. Carol M. Peacock: a system for planning and rotational delivery of intensity-modulated fields. Int J Imag Sys Technol 1995;6:56–61.

6. Ling CC, Burman C, Chui CS, et al. Conformal radiation treatment of prostate cancer using inversely-planned intensity-modulated photon beams produced with dynamic multileaf collimation. Int J Radiat Oncol Biol Phys 1996;35(4): 721–730.

7. Ling CC, Burman C, Chui CS, et al. Implementation of photon IMRT with dynamic leaf MLC for the treatment of prostate cancer. In: Sternick ES, ed. The Theory and Practice of Intensity-Modulated Radiation Therapy. Pittsburgh: NOMOS, 1997:219–228.

8. Mell LK, Mehrotra AK, Mundt AJ. Intensity-modulated radiation therapy use in the U.S., 2004. Cancer 2005;104(6):1296–1303.

9. Mell LK, Roeske JC, Mundt AJ. A survey of intensity-modulated radiation therapy use in the United States. Cancer 2003;98(1):204–211.

10. Bortfeld T, Schlegel W. Optimization of beam orientations in radiation therapy: some theoretical considerations. Phys Med Biol 1993;38(2):291–304.

11. Webb S. Optimizing the planning of intensity-modulated radiotherapy. Phys Med Biol 1994;39(12):2229–2246.

12. Soderstrom S, Brahme A. Optimization of the dose delivery in a few field techniques using radiobiological objective functions. Med Phys 1993;20(4):1201–1210.

13. Bai YR, Wu GH, Guo WJ, et al. Intensity modulated radiation therapy and chemotherapy for locally advanced pancreatic cancer: results of feasibility study. World J Gastroenterol 2003;9(11):2561–2564.

14. Lauve A, Morris M, Schmidt-Ullrich R, et al. Simultaneous integrated boost intensity-modulated radiotherapy for locally advanced head-and-neck squamous cell carcinomas: II--clinical results. Int J Radiat Oncol Biol Phys 2004;60(2):374–387.

15. Orlandi E, Palazzi M, Pignoli E, Fallai C, Giostra A, Olmi P. Radiobiological basis and clinical results of the simultaneous integrated boost (SIB) in intensity modulated radiotherapy (IMRT) for head and neck cancer: a review. Crit Rev Oncol Hematol 2010;73(2):111–125.

16. Castadot P, Lee JA, Geets X, Gregoire V. Adaptive radiotherapy of head and neck cancer. Semin Radiat Oncol 2010;20(2):84–93.

17. Mechalakos J, Lee N, Hunt M, Ling CC, Amols HI. The effect of significant tumor reduction on the dose distribution in intensity modulated radiation therapy for head-and-neck cancer: a case study. Med Dosim 2009;34(3):250–255.

18. Mohan R, Wu Q, Manning M, Schmidt-Ullrich R. Radiobiological considerations in the design of fractionation strategies for intensity-modulated radiation therapy of head and neck cancers. Int J Radiat Oncol Biol Phys 2000;46(3):619–630.

19. Barth NH. An inverse problem in radiation therapy. Int J Radiat Oncol Biol Phys 1990;18(2):425–431.

20. Bortfeld T, Burkelbach J, Boesecke R, Schlegel W. Methods of image reconstruction from projections applied to conformation radiotherapy. Phys Med Biol 1990;35(10):1423–1434.

21. Bortfeld T, Burkelbach J, Boesecke R, et al. Three-dimensional solution of the inverse problem in conformation radiotherapy. In: Breit A, ed. Advanced Radiation Therapy: Tumor Response Monitoring and Treatment Planning. Berlin: Springer Verlag, 1992:503–508.

22. Bortfeld TR, Kahler DL, Waldron TJ, Boyer AL. X-ray field compensation with multileaf collimators. Int J Radiat Oncol Biol Phys 1994;28(3):723–730.

23. Brahme A. Optimization of stationary and moving beam radiation therapy techniques. Radiother Oncol 1988;12(2): 129–140.

24. Brahme A, Roos JE, Lax I. Solution of an integral equation encountered in rotation therapy. Phys Med Biol 1982;27(10):1221–1229.

25. Carol MP. Peacock (TM) – a system for planning and rotational delivery of intensity-modulated fields. Int J Imaging Syst Technol 1995;6(1):56–61.

26. Carol M, Grant WH, Pavord D, et al. Initial clinical experience with the Peacock intensity modulation of a 3-D conformal radiation therapy system. Stereotactic Funct Neurosurg 1996;66(1-3):30–34.

27. Chen Z, Wang X, Bortfeld T, Mohan R, Reinstein L. The influence of scatter on the design of optimized intensity modulations. Med Phys 1995;22(11 Pt 1):1727–1733.

28. Chui CS, LoSasso T, Spirou S. Dose calculation for photon beams with intensity modulation generated by dynamic jaw or multileaf collimations. Med Phys 1994;21(8):1237–1244.

29. Cormack AM. A problem in rotation therapy with X rays. Int J Radiat Oncol Biol Phys 1987;13(4):623–630.

30. Cormack AM, Cormack RA. A problem in rotation therapy with X-rays: dose distributions with an axis of symmetry. Int J Radiat Oncol Biol Phys 1987;13(12):1921–1925.

31. Holmes T, Mackie TR. A filtered backprojection dose calculation method for inverse treatment planning. Med Phys 1994;21(2):303–313.

32. Kallman P, Lind B, Eklof A, Brahme A. Shaping of arbitrary dose distributions by dynamic multileaf collimation. Phys Med Biol 1988;33(11):1291–1300.

33. Mackie TR, Holmes T, Swerdloff S, et al. Tomotherapy: a new concept for the delivery of dynamic conformal radiotherapy. Med Phys 1993;20(6):1709–1719.

34. Mohan R, Leibel SA. Intensity modulation of the radiation beam. In: DeVita VT, Hellman S, Rosenberg SA, eds. Cancer: Principles and Practice of Oncology. Philadelphia: Lippincott-Raven, 1997:3093–3106.

35. Mohan R, Ling CC, Stein J, Wang XH. The number of beams in intensity-modulated treatments: In response to Drs. Soderstrom and Brahme. Int J Radiat Oncol Biol Phys 1996;34(3):758–759.

36. Mohan R, Wang X, Jackson A, et al. The potential and limitations of the inverse radiotherapy technique. Radiother Oncol 1994;32(3):232–248.

37. Soderstrom S, Brahme A. Selection of suitable beam orientations in radiation therapy using entropy and Fourier transform measures. Phys Med Biol 1992;37(4):911–924.

38. Spirou SV, Chui CS. Generation of arbitrary intensity profiles by dynamic jaws or multileaf collimators. Med Phys 1994;21(7):1031–1041.

39. Spirou SV, Chui CS. Generation of arbitrary intensity profiles by combining the scanning beam with dynamic multileaf collimation. Med Phys 1996;23(1):1–8.

40. Stein J, Bortfeld T, Dorschel B, Schlegel W. Dynamic X-ray compensation for conformal radiotherapy by means of multi-leaf collimation. Radiother Oncol 1994;32(2): 163–173.

41. van Santvoort JP, Heijmen BJ. Dynamic multileaf collimation without ‘tongue-and-groove’ underdosage effects. Phys Med Biol 1996;41(10):2091–2105.

42. Wang X, Spirou S, LoSasso T, Stein J, Chui CS, Mohan B. Dosimetric verification of intensity-modulated fields. Med Phys 1996;23(3):317–327.

43. Wang XH, Mohan R, Jackson A, Leibel SA, Fuks Z, Ling CC. Optimization of intensity-modulated 3D conformal treatment plans based on biological indices. Radiother Oncol 1995;37(2):140–152.

44. Webb S. Optimisation of conformal radiotherapy dose distributions by simulated annealing. Phys Med Biol 1989;34(10):1349–1370.

45. Webb S. Optimization of conformal radiotherapy dose distributions by simulated annealing: 2. Inclusion of scatter in the 2D technique. Phys Med Biol 1991;36(9):1227–1237.

46. Webb S, Bortfeld T, Stein J, Convery D. The effect of stair-step leaf transmission on the ’tongue-and-groove problem’ in dynamic radiotherapy with a multileaf collimator. Phys Med Biol 1997;42(3):595–602.

47. Yu CX. Intensity-modulated arc therapy with dynamic multileaf collimation: an alternative to tomotherapy. Phys Med Biol 1995;40(9):1435–1449.

48. Mackie TR. History of tomotherapy. Phys Med Biol 2006;51(13):R427–R453.

49. Palma DA, Verbakel WFAR, Otto K, Senan S. New developments in arc radiation therapy: a review. Cancer Treat Rev 2010;36(5):393–399.

50. Chao KSC, Deasy JO, Markman J, et al. A prospective study of salivary function sparing in patients with head-and-neck cancers receiving intensity-modulated or three-dimensional radiation therapy: initial results. Int J Radiat Oncol Biol Phys 2001;49(4):907–916.

51. Mohan R, Wu Q, Wang X, Stein J. Intensity modulation optimization, lateral transport of radiation, and margins. Med Phys 1996;23(12):2011–2021.

52. Palma D, Vollans E, James K, et al. Volumetric modulated arc therapy for delivery of prostate radiotherapy: comparison with intensity-modulated radiotherapy and three-dimensional conformal radiotherapy. Int J Radiat Oncol Biol Phys 2008;72(4):996–1001.

53. Jin JY, Wen N, Ren L, Glide-Hurst C, Chetty IJ. Advances in treatment techniques: arc-based and other intensity modulated therapies. Cancer J 2011;17(3):166–176.

54. Fraass BA, Kessler ML, McShan DL, et al. Optimization and clinical use of multisegment intensity-modulated radiation therapy for high-dose conformal therapy. Semin Radiat Oncol 1999;9(1):60–77.

55. Mohan R, Mageras GS, Baldwin B, et al. Clinically relevant optimization of 3-D conformal treatments. Med Phys 1992;19(4):933–944.

56. Kak AC, Slaney M. Principles of Computerized Tomographic Imaging, New York. IEEE Press, 1988.

57. Bradbury M, Hricak H. Molecular MR imaging in oncology. Magn Reson Imag Clin N Am 2005;13:225–240.

58. Gregoire V, Haustermans K, Lee JJ. Molecular image-guided radiotherapy with positron emission tomography. In: Joiner MC, van der Kogal A, eds. Basic Clinical Radiobiology. London: Hodder Arnold, 2009:271–286.

59. Apisarnthanarax S, Chao KSC. Current Imaging paradigms in radiation oncology. Radiat Res 2005;163(1):1–25.

60. Frank SJ, Chao KSC, Schwartz DL, Weber RS, Apisarnthanarax S, Macapinlac HA. Technology insight: PET and PET/CT in head and neck tumor staging and radiation therapy planning. Nat Clin Pract Oncol 2005;2(10):526–533.

61. Cheebsumon P, Yaqub M, van Velden FHP, Hoekstra OS, Lammertsma AA, Boellaard R. Impact of (18)F FDG PET imaging parameters on automatic tumour delineation: need for improved tumour delineation methodology. Eur J Nucl Med Mol Imaging 2011;38(12):2136–2144.

62. Moeller BJ, Rana V, Cannon BA, et al. Prospective risk-adjusted (18)F fluorodeoxyglucose positron emission tomography and computed tomography assessment of radiation response in head and neck cancer. J Clin Oncol 2009;27(15):2509–2515.

63. Chao KSC, Bhide S, Chen H, et al. Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach. Int J Radiat Oncol Biol Phys 2007;68(5):1512–1521.

64. Hong TS, Tome WA, Harari PM. Heterogeneity in head and neck IMRT target design and clinical practice. Radiother Oncol 2012;103(1):92–98.

65. Spirou SV, Chui CS. A gradient inverse planning algorithm with dose-volume constraints. Med Phys 1998;25(3):321–333.

66. Chui CS, Spirou S, LoSasso T. Testing of dynamic multileaf collimation. Med Phys 1996;23(5):635–641.

67. Chao KSC, Low DA, Perez CA, Purdy JA. Intensity-modulated radiation therapy in head and neck cancers: The Mallinckrodt experience. Int J Cancer 2000;90(2):92–103.

68. Intensity Modulated Radiation Therapy Collaborative Working Group. Intensity-modulated radiotherapy: current status and issues of interest. Int J Radiat Oncol Biol Phys 2001;51(4):880–914.

69. Sharpe MB, Miller BM, Yan D, Wong JW. Monitor unit settings for intensity modulated beams delivered using a step-and-shoot approach. Med Phys 2000;27(12):2719–2725.

70. Ehrgott M, Guler C, Hamacher HW, Shao LZ. Mathematical optimization in intensity modulated radiation therapy. Ann Oper Res 2010;175(1):309–365.

71. Kestin LL, Sharpe MB, Frazier RC, et al. Intensity modulation to improve dose uniformity with tangential breast radiotherapy: initial clinical experience. Int J Radiat Oncol Biol Phys 2000;48(5):1559–1568.

72. Thilmann C, Zabel A, Nill S, et al. Intensity-modulated radiotherapy of the female breast. Med Dosim 2002;27(2):79–90.

73. Vineberg KA, Eisbruch A, Coselmon MM, McShan DL, Kessler ML, Fraass BA. Is uniform target dose possible in IMRT plans in the head and neck? Int J Radiat Oncol Biol Phys 2002;52(5):1159–1172.

74. Xia P, Pickett B, Vigneault E, Verhey LJ, Roach M. Forward or inversely planned segmental multileaf collimator IMRT and sequential tomotherapy to treat multiple dominant intraprostatic lesions of prostate cancer to 90 GY. Int J Radiat Oncol Biol Phys 2001;51(1):244–254.

75. Mohan R, Arnfield M, Tong S, Wu Q, Siebers J. The impact of fluctuations in intensity patterns on the number of monitor units and the quality and accuracy of intensity modulated radiotherapy. Med Phys 2000;27(6):1226–1237.

76. Siebers JV, Lauterbach M, Keall PJ, Mohan R. Incorporating multi-leaf collimator leaf sequencing into iterative IMRT optimization. Med Phys 2002;29(6):952–959.

77. Das S, Cullip T, Tracton G, et al. Beam orientation selection for intensity-modulated radiation therapy based on target equivalent uniform dose maximization. Int J Radiat Oncol Biol Phys 2003;55(1):215–224.

78. De Gersem W, Claus F, De Wagter C, De Neve W. An anatomy-based beam segmentation tool for intensity-modulated radiation therapy and its application to head-and-neck cancer. Int J Radiat Oncol Biol Phys 2001;51(3):849–859.

79. Shepard DM, Earl MA, Li XA, Naqvi S, Yu C. Direct aperture optimization: a turnkey solution for step-and-shoot IMRT. Med Phys 2002;29(6):1007–1018.

80. Lomax AJ, Boehringer T, Coray A, Egger E, Goitein G, Grossmann M, et al. Intensity modulated proton therapy: A clinical example. Med Phys 2001;28(3):317–324.

81. Claus F, De Gersem W, De Wagter C, et al. An implementation strategy for IMRT of ethmoid sinus cancer with bilateral sparing of the optic pathways. Int J Radiat Oncol Biol Phys 2001;51(2):318–331.

82. Gregoire VP, Scallkiet P, Ang KK. Clinical Target Volumes in Conformal and Intensity Modulated Radiation Therapy. A Clinical Guide to Cancer Treatment. Berlin: Springer Verlag, 2003.

83. Staffurth J, Board RD. A review of the clinical evidence for intensity-modulated radiotherapy. Clin Oncol 2010;22(8):643–657.

84. Withers HR, Thames HD. Dose fractionation and volume effects in normal-tissues and tumors. Am J Clin Oncol-Cancer Clin Trials 1988;11(3):313–329.

85. Withers HR, Taylor JMG, Maciejewski B. Treatment volume and tissue tolerance. Int J Radiat Oncol Biol Phys 1988;14(4):751–759.

86. Li JG, Xing L, Boyer AL, Hamilton RJ, Spelbring DR, Turian JV. Matching photon and electron fields with dynamic intensity modulation. Med Phys 1999;26(11):2379–2384.

87. Maciejewski B, Withers HR, Taylor JM, Hliniak A. Dose fractionation and regeneration in radiotherapy for cancer of the oral cavity and oropharynx: tumor dose-response and repopulation. Int J Radiat Oncol Biol Phys 1989;16(3):831–843.

88. Withers HR, Peters LJ, Taylor JM, et al. Late normal tissue sequelae from radiation therapy for carcinoma of the tonsil: patterns of fractionation study of radiobiology. Int J Radiat Oncol Biol Phys 1995;33(3):563–568.

89. Withers HR, Peters LJ, Taylor JM, et al. Local control of carcinoma of the tonsil by radiation therapy: an analysis of patterns of fractionation in nine institutions. Int J Radiat Oncol Biol Phys 1995;33(3):549–562.

90. Withers HR, Taylor JM, Maciejewski B. The hazard of accelerated tumor clonogen repopulation during radiotherapy. Acta Oncol 1988;27(2):131–146.

91. Fowler JF. The linear-quadratic formula and progress in fractionated radiotherapy. Br J Radiol 1989;62(740):679–694.

92. Eisbruch A, Harris J, Garden AS, et al. Multi-institutional trial of accelerated hypofractionated intensity-modulated radiation therapy for early-stage oropharyngeal cancer (RTOG 00-22). Int J Radiat Oncol Biol Phys 2010;76(5): 1333–1338.

93. Wu Q, Mohan R, Morris M, Lauve A, Schmidt-Ullrich R. Simultaneous integrated boost intensity-modulated radiotherapy for locally advanced head-and-neck squamous cell carcinomas. I: dosimetric results. Int J Radiat Oncol Biol Phys 2003;56(2):573–585.

94. Chao KSC, Ozyigit G, Tran BN, Cengiz M, Dempsey JF, Low DA. Patterns of failure in patients receiving definitive and postoperative IMRT for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2003;55(2):312–321.

95. Mendenhall WM, Riggs CE, Vaysberg M, Amdur RJ, Werning JW. Altered fractionation and adjuvant chemotherapy for head and neck squamous cell carcinoma. Head Neck 2010;32(7):939–945.

96. Pugachev AB, Boyer AL, Xing L. Beam orientation optimization in intensity-modulated radiation treatment planning. Med Phys 2000;27(6):1238–1245.

97. Stein J, Mohan R, Wang XH, et al. Number and orientations of beams in intensity-modulated radiation treatments. Med Phys 1997;24(2):149–160.

98. Webb S. The physical basis of IMRT and inverse planning. Br J Radiol 2003;76(910):678–689.

99. Deasy JO. Multiple local minima in radiotherapy optimization problems with dose-volume constraints. Med Phys 1997;24(7):1157–1161.

100. Mageras GS, Mohan R. Application of fast simulated annealing to optimization of conformal radiation treatments. Med Phys 1993;20(3):639–647.

101. Webb S. Optimization by simulated annealing of 3-dimensional, conformal treatment planning for radiation-fields defined by a multileaf collimator. 2. Inclusion of 2-dimensional modulation of the x-ray intensity. Phys Med Biol 1992;37(8):1689–1704.

102. Pratx G, Xing L. GPU computing in medical physics: a review. Med Phys 2011;38(5):2685–2697.

103. Mohan R, Bortfeld T. The potential and limitations of IMRT: A physicist’s point of view. In: Bortfeld T, Schmidt-Ullrich R, De Neve W, Wazer DE, eds. Image-Guided IMRT. Heidelberg: Springer, 2006:11–18.

104. Swisher SG, Hofstetter W, Komaki R, et al. Improved long-term outcome with chemoradiotherapy strategies in esophageal cancer. Ann Thorac Surg 2010;90(3):892–898.

105. Broderick M, Leech M, Coffey M. Direct aperture optimization as a means of reducing the complexity of intensity modulated radiation therapy plans. Radiat Oncol 2009;4: 8 doi:10.1186/1748-717X-4-8.

106. Dogan N, Leybovich LB, King S, Sethi A, Emami B. Improvement of treatment plans developed with intensity-modulated radiation therapy for concave-shaped head and neck tumors. Radiology 2002;223(1):57–64.

107. Wu Q, Mohan R. Multiple local minima in IMRT optimization based on dose-volume criteria. Med Phys 2002;29(7):1514–1527.

108. Ezzell GA. Genetic and geometric optimization of three-dimensional radiation therapy treatment planning. Med Phys 1996;23(3):293–305.

109. Xu F, Mueller K. Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware. IEEE Trans Nucl Sci 2005;52(3):654–663.

110. Bortfeld T, Schlegel W, Dykstra C, Levegrun S, Preiser K. Physical vs. biological objectives for treatment plan optimization. Radiother Oncol 1996;40(2):185–187.

111. Carrasco P, Jornet N, Duch MA, et al. Comparison of dose calculation algorithms in phantoms with lung equivalent heterogeneities under conditions of lateral electronic disequilibrium. Med Phys 2004;31(10):2899–2911.

112. Wu Y, Yan D, Sharpe MB, Miller B, Wong JW. Implementing multiple static field delivery for intensity modulated beams. Med Phys 2001;28(11):2188–2197.

113. Adkison JB, Khuntia D, Bentzen SM, et al. Dose escalated, hypofractionated radiotherapy using helical tomotherapy for inoperable non-small cell lung cancer: preliminary results of a risk-stratified phase I dose escalation study. Technol Cancer Res Treat 2008;7(6):441–447.

114. De Ruysscher D, Wanders R, van Haren E, et al. HI-CHART: a phase I/II study on the feasibility of high-dose continuous hyperfractionated accelerated radiotherapy in patients with inoperable non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2008;71(1):132–138.

115. Mavroidis P, Shi C, Plataniotis GA, et al. Comparison of the helical tomotherapy against the multileaf collimator-based intensity-modulated radiotherapy and 3D conformal radiation modalities in lung cancer radiotherapy. Br J Radiol 2011;84(998):161–172.

116. Marnitz S, Lukarski D, Kohler C, et al. Helical tomotherapy versus conventional intensity-modulated radiation therapy for primary chemoradiation in cervical cancer patients: an intraindividual comparison. Int J Radiat Oncol Biol Phys 2011;81(2):424–430.

117. Mavroidis P, Ferreira BC, Shi C, Delichas MG, Lind BK, Papanikolaou N. Comparison of the helical tomotherapy and MLC-based IMRT radiation modalities in treating brain and cranio-spinal tumors. Technol Cancer Res Treat 2009;8(1):3–14.

118. Murthy V, Master Z, Gupta T, et al. Helical tomotherapy for head and neck squamous cell carcinoma: Dosimetric comparison with linear accelerator-based step-and-shoot IMRT. J Cancer Res Therapeutics 2010;6(2):194–198.

119. Chen AM, Marsano J, Perks J, et al. Comparison of IMRT techniques in the radiotherapeutic management of head and neck cancer: is tomotherapy “better” than step-and-shoot IMRT? Technol Cancer Res Treat 2011;10(2):171–177.

120. Convery DJ, Rosenbloom ME. The generation of intensity-modulated fields for conformal radiotherapy by dynamic collimation. Phys Med Biol 1992;37(6):1359–1374.

121. Svensson R, Kallman P, Brahme A. An analytical solution for the dynamic control of multileaf collimators. Phys Med Biol 1994;39(1):37–61.

122. Xia P, Verhey LJ. Multileaf collimator leaf sequencing algorithm for intensity modulated beams with multiple static segments. Med Phys 1998;25(8):1424–1434.

123. Que W. Comparison of algorithms for multileaf collimator field segmentation. Med Phys 1999;26(11):2390–2396.

124. Dai J, Zhu Y. Minimizing the number of segments in a delivery sequence for intensity-modulated radiation therapy with a multileaf collimator. Med Phys 2001;28(10): 2113–2120.

125. Langer M, Thai V, Papiez L. Improved leaf sequencing reduces segments or monitor units needed to deliver IMRT using multileaf collimators. Med Phys 2001;28(12):2450–2458.

126. Hardcastle N, Metcalfe P, Ceylan A, Williams MJ. Multileaf collimator end leaf leakage: implications for wide-field IMRT. Phys Med Biol 2007;52(21):N493–N504.

127. Kim JO, Siebers JV, Keall PJ, Arnfield MR, Mohan R. A Monte Carlo study of radiation transport through multileaf collimators. Med Phys 2001;28(12):2497–2506.

128. Siebers JV, Keall PJ, Kim JO, Mohan R. A method for photon beam Monte Carlo multileaf collimator particle transport. Phys Med Biol 2002;47(17):3225–3249.

129. Klein EE, Hanley J, Bayouth J, et al. Task group 142 report: quality assurance of medical accelerators. Med Phys 2009;36(9):4197–4212.

130. LoSasso T, Chui CS, Ling CC. Comprehensive quality assurance for the delivery of intensity modulated radiotherapy with a multileaf collimator used in the dynamic mode. Med Phys 2001;28(11):2209–2219.

131. Kubo HD, Hill BC. Respiration gated radiotherapy treatment: a technical study. Phys Med Biol 1996;41(1):83–91.

132. Keall P, Vedam S, George R, et al. The clinical implementation of respiratory-gated intensity-modulated radiotherapy. Med Dosim 2006;31(2):152–162.

133. Chang J, Mageras GS, Yorke E, et al. Observation of interfractional variations in lung tumor position using respiratory gated and ungated megavoltage cone-beam computed tomography. Int J Radiat Oncol Biol Phys 2007;67(5):1548–1558.

134. Mageras GS, Yorke E, Rosenzweig K, et al. Fluoroscopic evaluation of diaphragmatic motion reduction with a respiratory gated radiotherapy system. J Appl Clin Med Phys/Am Coll Med Phys 2001;2(4):191–200.

135. Halperin EC, Wazer DE, Perez CA, Brady LW. Principles and Practice of Radiation Oncology, 6th ed. Philadelphia: Lippincott Williams & Wilkins, 2013.

136. Ezzell GA, Burmeister JW, Dogan N, et al. [AAPM Rept 119] IMRT commissioning: multiple institution planning and dosimetry comparisons, a report from AAPM Task Group 119. Med Phys 2009;36(11):5359–5373.

137. Low DA, Parikh P, Dempsey JF, Wahab S, Huq S. Ionization chamber volume averaging effects in dynamic intensity modulated radiation therapy beams. Med Phys 2003;30(7):1706–1711.

138. Kitamura K, Shirato H, Shimizu S, et al. Registration accuracy and possible migration of internal fiducial gold marker implanted in prostate and liver treated with real-time tumor-tracking radiation therapy (RTRT). Radiother Oncol 2002;62(3):275–281.

139. Murphy MJ. Fiducial-based targeting accuracy for external-beam radiotherapy. Med Phys 2002;29(3):334–344.

140. Lattanzi J, McNeeley S, Hanlon A, Schultheiss TE, Hanks GE. Ultrasound-based stereotactic guidance of precision conformal external beam radiation therapy in clinically localized prostate cancer. Urology 2000;55(1):73–78.

141. Morr J, DiPetrillo T, Tsai JS, Engler M, Wazer DE. Implementation and utility of a daily ultrasound-based localization system with intensity-modulated radiotherapy for prostate cancer. Int J Radiat Oncol Biol Phys 2002;53(5):1124–1129.

142. Serago CF, Chungbin SJ, Buskirk SJ, Ezzell GA, Collie AC, Vora SA. Initial experience with ultrasound localization for positioning prostate cancer patients for external beam radiotherapy. Int J Radiat Oncol Biol Phys 2002;53(5):1130–1138.

143. Mackie TR, Kapatoes J, Ruchala K, et al. Image guidance for precise conformal radiotherapy. Int J Radiat Oncol Biol Phys 2003;56(1):89–105.

144. Li H, Zhu XR, Zhang L, et al. Comparison of 2D radiographic images and 3D cone beam computed tomography for positioning head-and-neck radiotherapy patients. Int J Radiat Oncol Biol Physics 2008;71(3):916–925.

145. Lu J, Guerrero TM, Munro P, et al. Four-dimensional cone beam CT with adaptive gantry rotation and adaptive data sampling. Med Phys 2007;34(9):3520–3529.

146. Perks JR, Lehmann J, Chen AM, Yang CC, Stern RL, Purdy JA. Comparison of peripheral dose from image-guided radiation therapy (IGRT) using kV cone beam CT to intensity-modulated radiation therapy (IMRT). Radiother Oncol 2008;89(3):304–310.

147. Sillanpaa J, Chang J, Mageras G, et al. Developments in megavoltage cone beam CT with an amorphous silicon EPID: reduction of exposure and synchronization with respiratory gating. Med Phys 2005;32(3):819–829.

148. Xing L, Chen Y, Luxton G, Li JG, Boyer AL. Monitor unit calculation for an intensity modulated photon field by a simple scatter-summation algorithm. Phys Med Biol 2000;45(3):N1–N7.

149. Jursinic PA, Nelms BE. A 2-D diode array and analysis software for verification of intensity modulated radiation therapy delivery. Med Phys 2003;30(5):870–879.

150. Kung JH, Chen GT, Kuchnir FK. A monitor unit verification calculation in intensity modulated radiotherapy as a dosimetry quality assurance. Med Phys 2000;27(10):2226–2230.

151. Low DA. Quality assurance of intensity-modulated radiotherapy. Semin Radiat Oncol 2002;12(3):219–228.

152. Verhey LJ. Issues in optimization for planning of intensity-modulated radiation therapy. Semin Radiat Oncol 2002;12(3):210–218.

153. van Elmpt W, McDermott L, Nijsten S, Wendling M, Lambin P, Mijnheer B. A literature review of electronic portal imaging for radiotherapy dosimetry. Radiother Oncol 2008;88(3):289–309.

154. Mell LK, Song WY, Pawlicki T, Mundt AJ. Image-guided radiation therapy. In Halperin EC, Wazer DE, Perez CA, Brady LW, eds. Principles and Practice of Radiation Oncology, 6th ed. Philadelphia: Lippincott Williams & Wilkins, 2013: 246–276.

155. Allison RR, Gay HA, Mota HC, Sibata CH. Image-guided radiation therapy: current and future directions. Future Oncol (London, England) 2006;2(4):477–492.

156. Boda-Heggemann J, Lohr F, Wenz F, Flentje M, Guckenberger M. kV cone-beam CT-based IGRT: a clinical review. Strahlentherap Onkol 2011;187(5):284–291.

157. Sheng K, Chow J, Hunter G, Larner J, Read P. Is daily computed tomography image guidance necessary for nasal cavity and nasopharyngeal radiotherapy? An investigation based on helical tomotherapy. J Appl Clin Med Phys 2008;9(1):36–46.

158. Pouliot J, Bani-Hashemi A, Chen J, et al. Low-dose megavoltage cone-beam CT for radiation therapy. Int J Radiat Oncol Biol Phys 2005;61(2):552–560.

159. Hong TS, Welsh JS, Ritter MA, et al. Megavoltage computed tomography – an emerging tool for image-guided radiotherapy. Am J Clin Oncol-Cancer Clin Trials 2007;30(6): 617–623.

160. Imam SK. Review of positron emission tomography tracers for imaging of tumor hypoxia. Cancer Biother Radiopharm 2010;25(3):365–374.

161. Chao KSC, Bosch WR, Mutic S, et al. A novel approach to overcome hypoxic tumor resistance: Cu-ATSM-guided intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys 2001;49(4):1171–1182.

162. Zaidi H, Del Guerra A. An outlook on future design of hybrid PET/MRI systems. Med Phys 2011;38(10):5667–5689.

163. Gayou O, Miften M. Commissioning and clinical implementation of a mega-voltage cone beam CT system for treatment localization. Med Phys 2007;34(8):3183–3192.

164. Miften M, Gayou O, Reitz B, Fuhrer R, Leicher B, Parda DS. IMRT planning and delivery incorporating daily dose from mega-voltage cone-beam computed tomography imaging. Med Phys 2007;34(10):3760–3767.

165. Zucca S, Carau B, Solla I, et al. Prostate image-guided radiotherapy by megavolt cone-beam CT. Strahlenther Onkol 2011;187(8):473–478.

166. Alasti H, Cho YB, Vandermeer AD, et al. A novel four-dimensional radiotherapy method for lung cancer: imaging, treatment planning and delivery. Phys Med Biol 2006;51(12):3251–3267.

167. Starkschall G, Balter P, Britton K, McAleer MF, Cox JD, Mohan R. Interfractional reproducibility of lung tumor location using various methods of respiratory motion mitigation. Int J Radiat Oncol Biol Phys 2011;79(2):596–601.

168. Jiang SB. Technical aspects of image-guided respiration-gated radiation therapy. Med Dosim 2006;31(2):141–151.

169. Park I. A new approach to produce spread-out Bragg peak using the MINUIT fit. Curr Appl Phys 2009;9(4): 852–855.

170. Kanai T, Furusawa Y, Fukutsu K, Itsukaichi H, EguchiKasai K, Ohara H. Irradiation of mixed beam and design of spread-out Bragg peak for heavy-ion radiotherapy. Radiat Res 1997;147(1):78–85.

171. Dorr W, Joiner MC. Protons and other ions in radiotherapy. In: Joiner MC, van der Kogel A, eds. Basic Clinical Radiobiology. London: Hodder Arnold, 2009:332–338.

172. Gragoudas E, Li WJ, Goitein M, Lane AM, Munzenrider JE, Egan KM. Evidence-based estimates of outcome in patients irradiated for intraocular melanoma. Archiv Ophthalmol 2002;120(12):1665–1671.

173. Munzenrider JE, Liebsch NJ. Proton therapy for tumors of the skull base. Strahlenther Onkol 1999;175:57–63.

174. St Clair WH, Adams JA, Bues M, et al. Advantage of protons compared to conventional X-ray or IMRT in the treatment of a pediatric patient with medulloblastoma. Int J Radiat Oncol Biol Phys 2004;58(3):727–734.

175. Palm A, Johansson K-A. A review of the impact of photon and proton external beam radiotherapy treatment modalities on the dose distribution in field and out-of-field; implications for the long-term morbidity of cancer survivors. Acta Oncolog 2007;46(4):462–473.

176. Chan AW, Liebsch NJ. Proton radiation therapy for head and neck cancer. J Surg Oncol 2008;97(8):697–700.

177. Ramaekers BL, Pijls-Johannesma M, Joore MA, et al. Systematic review and meta-analysis of radiotherapy in various head and neck cancers: comparing photons, carbon-ions and protons. Cancer Treat Rev 2011;37(3):185–201.

178. Ares C, Khan S, MacArtain AM, et al. Postoperative proton radiotherapy for localized and locoregional breast cancer: potential for clinically relevant improvements? Int J Radiat Oncol Biol Phys 2010;76(3):685–697.

179. Bouchard M, Amos RA, Briere TM, Beddar S, Crane CH. Dose escalation with proton or photon radiation treatment for pancreatic cancer. Radiother Oncol 2009;92(2):238–243.

180. Chera BS, Vargas C, Morris CG, et al. Dosimetric study of pelvic proton radiotherapy for high-risk prostate cancer. Int J Radiat Oncol Biol Phys 2009;75(4):994–1002.

181. Nguyen PL, Trofimov A, Zietman AL. Proton-beam vs intensity-modulated radiation therapy – which is best for treating prostrate cancer? Oncology-New York 2008;22(7): 748–754.

182. Petersen JBB, Lassen Y, Hansen AT, Muren LP, Grau C, Hoyer M. Normal liver tissue sparing by intensity-modulated proton stereotactic body radiotherapy for solitary liver tumours. Acta Oncol 2011;50(6):823–828.

183. van de Water TA, Lomax AJ, Bijl HP, et al. Potential benefits of scanned intensity-modulated proton therapy versus advanced photon therapy with regard to sparing of the salivary glands in oropharyngeal cancer. Int J Radiat Oncol Biol Phys 2011;79(4):1216–1224.

184. Widesott L, Pierelli A, Fiorino C, et al. Intensity-modulated proton therapy versus helical tomotherapy in nasopharynx cancer: planning comparison and NTCP evaluation. Int J Radiat Oncol Biol Phys 2008;72(2):589–596.

185. Widesott L, Pierelli A, Fiorino C, et al. Helical tomotherapy vs. intensity-modulated proton therapy for whole pelvis irradiation in high-risk prostate cancer patients: dosimetric, normal tissue complication probability, and generalized equivalent uniform dose analysis. Int J Radiat Oncol Biol Phys2011;80(5):1589–1600.

186. Luo W, Li J, Fourkal E, Fan J, et al. Dosimetric advantages of IMPT over IMRT for laser-accelerated proton beams. Phys Med Biol 2008;53(24):7151–7166.

187. Kosaki K, Ecker S, Habermehl D, et al. Comparison of intensity modulated radiotherapy (IMRT) with intensity modulated particle therapy (IMPT) using fixed beams or an ion gantry for the treatment of patients with skull base meningiomas. Radiat Oncol 2012;7:44.

188. Welsh J, Gomez D, Palmer MB, et al. Intensity-modulated proton therapy further reduces normal tissue exposure during definitive therapy for locally advanced distal esophageal tumors: a dosimetric study. Int J Radiat Oncol Biol Phys 2011;81(5):1336–1342.

189. Cotter SE, Herrup DA, Friedmann A, et al. Proton radiotherapy for pediatric bladder/prostate rhabdomyosarcoma: clinical outcomes and dosimetry compared to intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys 2011;81(5):1367–1373.

190. Sheets NC, Goldin GH, Meyer AM, Wu Y, Chang Y, Sturmer T, et al. Intensity-modulated radiation therapy, proton therapy, or conformal radiation therapy and morbidity and disease control in localized prostate cancer. JAMA 2012;307(15):1611–1620.

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