Patient Setup, Immobilization, and Image Acquisition

The minimization of patient set-up uncertainty is more important in 3D-CRT than in conventional radiotherapy because of the improved conformality of the dose distribution (i.e., quick dose gradient between the boundary of tumor volume and normal tissue). This becomes even more critical in IMRT because IMRT plans often produce even sharper dose gradients at the boundary between the tumor volume and normal tissue.¹⁶ Thus, immobilization devices and precise patient positioning procedures must be applied throughout the process of image acquisition, simulation, and treatment. Many different techniques and devices are in use to achieve this, including conventional devices such as vacuum cradles, plaster casts, face masks, and so on. More elaborate devices specific to IMRT are also being introduced such as immobilization devices that attach directly to the treatment couch to ensure that patients are positioned in the same location during each daily treatment. The same “index positioning method” is also used during the CT image acquisition. Stereotactic body frames have also been developed to achieve set-up accuracy approaching that of stereotactic radiosurgery.

In addition to immobilizing the patient, frequent verifications with image guidance are performed to ensure precise reproducibility of the patient position and thus ensure accurate treatment delivery. The first step of image guidance for radiotherapy is to acquire a treatment planning CT, which represents the patient’s body (external anatomy) and internal anatomy. This step is often referred to as CT simulation. CT simulation is performed with a CT scanner with capabilities for spiral or helical scanning and volumetric data acquisition combined with a laser localization system similar to the laser system on the treatment machine such as a linear accelerator, and a high-speed graphic workstation that emulates a treatment planning computer. The laser localization system permits marking the treatment isocenter with fiduciaries on the patient skin, which defines the initial reference point for the entire treatment. It is important that the CT image set is obtained with the patient in the treatment position on a flat couch that is geometrically identical to the couch on which the patient will ultimately be treated. The high-speed workstation provides the capabilities of rapid image reconstruction, target volume and normal tissue delineation, and 3-D beam geometry display. During the CT simulation, the patient’s anatomy is reconstructed from the CT data and displayed in BEV according to the specific treatment set-up parameters. Digitally reconstructed radiographic images provide projected images at a specific view, helping in decision-making and documentation. For moving organs, such as the lungs and liver, new generations of CT scanners with multiple arrays of detectors are capable of acquiring 4D-CT images, providing dynamic views of these moving organs. Digitally reconstructed fluoroscopy from 4D-CT can be displayed as a movie, allowing radiation oncologists to measure the magnitude of the organ or tumor motion in any direction. Furthermore, if the CT images are acquired in synchronization with the breathing cycle, the cine mode of CT images can be constructed into multiple breathing phases, typically eight to ten. To synchronize the CT acquisition with the patient’s breathing cycle, a breathing sensor—either a belt attached to the patient’s chest or an infrared sensor placed on the patient’s chest—is needed.

PET/CT simulation is similar to the CT simulation except the procedure is carried out on a combined PET/CT scanner; again, a flat tabletop and alignment lasers are necessary for the purpose of radiotherapy treatment planning. Direct PET/CT simulation avoids the additional planning CT acquisition, improves the workflow, and reduces the potential errors during image registration if a PET scan is acquired separately or if a PET/CT scan is acquired in a nontreatment position.¹⁷

For some cancer sites (e.g., the prostate), MRI provides much better soft tissue contrast than CT images. MRI simulation can be desirable for reasons similar to the desirability of PET/CT. MRI scanners used in radiology are close scanners, making them inaccessible to the patient once the patient is in the scanner. To set up the patient in a desired treatment position and with a proper immobilization device, an open MRI simulator is preferred. Chen and colleagues¹⁸ reported their clinical implementation using a 0.23-tesla (T) open MRI scanner for prostate patients. The MRI scanner consists of two poles, which are each approximately 1 meter in diameter. The separation between the two poles is 47 cm. The MRI scanner table can move in and out along a set of rails mounted on the floor and laterally on an orthogonal set of rails built in the couch. Vertical movement of the table is made by using a composite flat top with hard foam spacers beneath the patient. A set of three triangulation lasers (center and laterals) identical to those used on linear accelerators has been used for patient positioning.

The challenge of directly using MRI for radiotherapy treatment planning is that MRI does not contain the electron density information needed for the dose calculation and image distortion in part because of the inhomogeneities in the main magnetic and gradient fields, and in part because of the differentiation in chemical shift and susceptibility of fat and water. With a low magnetic field of less than 0.5 T, Chen and colleagues¹⁸ report that the latter effect is reduced to 1 to 2 pixels. The distortion caused by the inhomogeneity of the magnetic fields increases as the patient size increases. Even with the distortion correction algorithm, residual distortion was reported greater than 2 cm for lateral patient size larger than 40 cm, whereas the distortion for patient lateral size smaller than 38 cm is insignificant dosimetrically.

Delineation of Treatment Volume and Critical Organs

Treatment volumes are defined on the CT images according to International Commission on Radiation Units and Measurements Report 62 nomenclature,¹⁹ with the gross target volume (GTV) defined as the visible tumor as seen on CT or other imaging studies; the clinical target volume (CTV) as the visualized tumor plus regions at risk such as microscopic extension of disease, nodal chains, and so on; and the planning target volume (PTV) as a CTV expanded to include setup errors, patient motion, linear accelerator alignment errors, and other uncertainties. For motion tumor volume, internal tumor volume includes magnitude of movement of the tumor. The manual delineation of CTV, PTV, plus adjacent critical organs is time consuming and subject to large variations among observers. The Radiation Therapy Oncology Group and other groups of experts attempted to establish a consensus to minimize these variations.^20,21 Many treatment planning systems now incorporate various software tools for performing automatic delineation of tissue structures, but these are of limited utility as current algorithms are only accurate for outlining external body contours and internal contours of very high contrast such as bone, lung, and air cavities. Other commercial programs and research institutions^22–26 developed deformable image registration algorithms, permitting the planner to map contours from a template case to a patient case with a similar tumor stage and site but different anatomic characteristics. Fig. 10-3 shows an example of computer-assisted contouring in pelvic lymph nodes. The deformable image registration tools are especially useful when adaptive planning is required for patients with anatomic changes during radiotherapy. Fig. 10-4 shows an example of using deformable image registration to generate contours from an early treatment planning CT (first CT) to the middle-course treatment planning CT (second CT) for a patient with nasopharyngeal cancer while comparing them with manually drawn contours on both CT images.

FIGURE 10-3 • Comparison of the manually drawn (A) and atlas-generated contours (B) of pelvic lymph nodal volumes displayed in an axial image and a coronal image.

FIGURE 10-4 • An example of using deformable image registration to generate contours from an early treatment-planning computed tomography (CT) to the middle-course treatment planning CT for a patient with nasopharyngeal cancer.

Although all of these tools improve the efficiency of laborious contouring and reduce differences in observations among practitioners, it is expected that human intervention will likely remain a necessary and important component for defining tumor volumes, especially the GTV. These tools can significantly improve consistency and efficiency of sensitive structures.

It should be emphasized that in IMRT, accurate delineation of the tumor volume and critical organs is even more important than in 3D-CRT because with inverse planning (IP) optimization the delineated contours are used as direct input to the computer optimization algorithm (as discussed later), which attempts to produce dose distributions conforming to the prescribed dose constraints of the delineated tumor and sensitive normal structures. For similar reasons, it is often necessary with IMRT and inverse treatment planning to contour many normal tissues in the vicinity of the PTV that would not normally be contoured for 3D-CRT treatment planning. This is necessary in order to “advise” the optimization algorithm as to where it is permissible and impermissible to deposit moderate or high doses. For example, in a typical head and neck case, in addition to the commonly contoured structures in 3D-CRT planning such as the spinal cord and brainstem, it may also be necessary to contour the parotid glands, optic structures, the oral cavity, the mandible, the inner ears, and so on, lest the optimization algorithms unwittingly deposit large doses to these volumes to meet dose constraints placed on other structures.

Selection of Treatment Beams

To facilitate selection of beam angles and field shapes based on the relative positions of various anatomic structures, various display schema have been developed for 3D-CRT to represent the PTV and the adjacent critical organs in 3-D perspective on a 2-D computer display monitor. The treatment geometry is usually best visualized in BEV when the anatomy is viewed from the perspective of the radiation source,^27,28 as shown in Fig. 10-5. Beam orientations are chosen by observing the patient in BEV from various beam directions and selecting those directions for which the PTV appears to be best separated from the normal tissues. The BEV, however, considers only the geometric property of the patient. The concept has been extended to BEV dosimetrics (BEVDs), in which both anatomy and dosimetric tolerances of the target and involved organs are taken into account for guiding the beam-placement process. BEVD objectively evaluates the goodness of angular space, and it has shown that the incorporation of the BEVD score in IMRT planning can greatly facilitate the selection of beam configuration.^29–33 In Fig. 10-6, we show the score functions for an IMRT treatment of a paraspinal tumor obtained with and without BEVD guidance.²⁹

FIGURE 10-5 • The beam’s eye view of a right-anterior oblique angle for a head-neck patient in a supine position with the beam shape as defined by a multileaf collimator (MLC). The planning target volume (PTV) is depicted in yellow, the brainstem in green, the spinal cord in purple, and the pituitary gland in red. Note how the MLC leaves (transparent green) are shaped to conform to the PTV.

FIGURE 10-6 • Isodose distributions of an intensity-modulated radiation therapy treatment with five equiangular beams (40°, 110°, 180°, 255°, 325°). The middle and bottom plots show the beam’s eye view score for the patient for coplanar and non-coplanar beam configurations, respectively.

(From Pugachev A, et al: Role of beam orientation optimization in intensity-modulated radiation therapy, Int J Radiat Oncol Biol Phys 50[2]:551–560, 2001.)

Clinically, selection of beams is based on a combination of experience, standard protocols, and patient-specific anatomy. For example, the relatively small variations from patient to patient in the anatomic relationship between the prostate and its nearby critical structures (most importantly the rectum, bladder, and urethra) enables IMRT prostate treatment plans to follow a more or less standard protocol with regard to beam selection. A treatment planning template can be developed for each specific cancer site.^33,34 For brain tumors, on the other hand, beam selection is depends on the patient-specific anatomy and the experience of the individual planner. In general, IMRT fields with large fluctuations in intensity require higher velocities and accelerations of MLC leaves during beam delivery. Selection of optimum beam directions is often more critical when treating PTVs with a complex shape or where there is minimal separation between the PTV and a critical normal structure. Carefully selecting optimum beam directions in IMRT may also reduce the complexity of intensity profiles (i.e., the magnitude of intensity gradients within the field), thus improving delivery accuracy and efficiency. Automation of beam configuration selection is still an active area of research. Unfortunately, at present, none of the available IMRT treatment planning systems can effectively determine optimum beam directions, and thus still rely on the treatment planner’s experience and intuition.

Forward Planning

In contrast to IP (discussed in the following section), forward treatment planning involves the planner manually selecting the number of beams, their directions and shapes, inclusion or exclusion of hard or dynamic wedges (wedges modulate beam intensity monotonically along a selected direction), and the relative weightings of each beam shape (the static beam intensities). The computer simply calculates the resultant dose distributions from these beam parameters. In the sense of FP, the dose distribution is predictable by the beam parameters set by the planners. This is fundamentally different than IP in which the human treatment planner specifies the desired dose constraints (ideally, the desired dose shapes or distributions) and the computer calculates the required beam intensities and shapes to best meet the specified dose constraints. The specified dose constraints are a simplified description of dose shapes or distributions because it is not intuitive to depict a desired dose distribution. Table 10-1 provides an example of the desired dose constraints of a concurrent treatment of the pelvic lymph nodes and prostate cancer plus a cone down to the prostate alone.

In either case, after the computer calculates the resultant dose distribution, the planner may choose to make adjustments to improve the plan. In FP, the planner adjusts the beam intensities (or weights) and may adjust the field shapes or directions, and then asks the computer to recalculate the dose distribution. This process, referred to as manual optimization, is repeated until an optimal dose distribution is obtained. The experience of the individual planner is critical in FP. Even for a skilled planner, the quality of the plan may be limited by the restricted number of degrees of freedom one has, particularly the constraint that intensity within each field must be uniform. Further, the number of iterations one can perform within the allotted time is clearly limited. For IP, the philosophy behind plan optimization is completely reversed.

The advantage of FP is in its ability to produce a simple plan with a small dose variation inside the tumor volume.^35,36 For example, in a treatment plan of a breast, Mayo and colleauges³⁷ compared 3D-CRT plans with wedges to FP-IMRT, IP-IMRT, and a hybrid of open fields and some IP fields. This study reported that the tangent-only IMRT plans, created with an IP method, resulted in the worst dose homogeneity inside the breast, the worst maximum dose outside the target, and 2.3 times more monitor units (MUs) than the FP-IMRT plans. In combining two open tangents with tangent IMRT beams,³⁷ it was found that a plan quality comparable to FP-IMRT could be achieved in significantly less planning time.

Inverse Planning

Cost Function of Inverse Planning

Central to the success of any optimization schema is the specification of an objective or cost function. The cost function is a mathematical definition of the “goodness” of a treatment plan, and the computer optimization algorithm attempts to minimize the cost function as it adjusts the beam weights from one iteration to the next. Objective functions (OFs) can be based on biologic criteria,^39,40 but more often are based on dose criteria.^41–43 Biologic-based OFs use a calculated radiobiologic response as a measure of the merit of a plan (with calculations based on some model that relates radiation dose plus volume of irradiated tissue to predicted biologic response). The use of biologically weighted OFs is in principle more relevant because treatment outcome is determined by the biologic response. However, a universally accepted biologic model that predicts treatment outcome is yet to be developed. In the meantime, it seems that a clinically sensible and feasible approach is to construct the OF empirically based on the clinical outcome data.^44,45

In reality, most popular IP algorithms are still relying on dose-based cost functions for historical reasons. Some commercial planning systems have recently started including the option of biologic model–based IP. The numerical value of a dose-based OF is calculated from some weighted average of the differences between delivered and prescribed doses for every voxel in every tissue defined in the treatment plan (i.e., the PTV plus all normal tissues for which a dose constraint has been specified).^41,43,46 The prescription dose to the target, specified tolerance doses to normal tissues, and weighting factors that reflect the importance of each tissue are designated as the constraints.

For the PTV, one of many possible OFs is:

in which Σ_i represents a summation over all voxels in the PTV and w_i equals the weighting (or penalty) factor for the i^th voxel.

Usually within a PTV all voxels would have equal weighting factors, but this does not have to be true—a voxel-specific penalty scheme can, in general, significantly improve the final dose distribution because of greatly enlarged solution space.^42,47 The calculated dose equals d_cal (using the current beam parameters) for the i^th voxel. The prescribed dose equals d_pres for the i^th voxel.

The exact formulation for the objective, however, could take many other algebraic forms. For example, the absolute value of (d_cal − d_pres)_i could be used rather than its square. Or one could include (d_cal − d_pres)_i in the summation only if (d_cal − d_pres)_i is negative; that is, one must assign a penalty only if the calculated dose is less than the prescribed dose. Conversely, when calculating the contribution to the OF for normal tissue, one would usually assign a penalty only if d_cal is larger than d_pres. The total OF is then the sum of the OFs for each tissue, weighted by the w_i values, which will likely differ for each tissue. For example, one would often assign a higher penalty or weighting factor to the spinal cord than to the rectum or bladder, as the former is a more radiosensitive structure with more disastrous consequences if overdosed. Automation of the weighting factors in IP has been reported by Xing and colleagues.⁴⁸

For many tissues, however, acceptable doses cannot be specified by a single dose value and dose-volume effects are often incorporated into the OF. A typical dose-volume constraint may be stated as “no more than q% of the particular organ may receive a dose greater than d.” This is equivalent to specifying a single point on a DVH with the constraint that the value of the DVH at a dose value of d must be less than q%. Most inverse treatment planning algorithms allow the planner to define multiple such points for each tissue, with different penalties assigned to each point if desired. DVH type constraints, rather than a single dose limit can also be assigned to the PTV if desired.

Several OFs are illustrated graphically in Fig. 10-7. For the PTV or target, the graph illustrates the concept of the “allowable inhomogeneity.” That is, if the dose is between a lower limit P_l and an upper limit P_u, no penalty is assessed. Also, a larger weight can be assigned to penalize underdose as opposed to overdose (or vice versa). For normal tissue, a penalty can be applied if the dose exceeds a certain critical value (D_c) or is based on dose-volume considerations, as represented schematically in the rightmost graph of Fig. 10-7.

FIGURE 10-7 • Examples of possible objective functions, illustrated graphically. For the planning target volume (leftmost graph) doses within the “allowable inhomogeneity” are permitted, with different penalties assessed for underdose as opposed to overdose. For the normal organ, a penalty can be applied if the dose exceeds a certain critical value (D_c) (middle graph), or based on dose-volume considerations (rightmost graph).

Thus dose constraints are the prescribed dose and allowable dose inhomogeneities to the PTV plus the dose limits to various sensitive structures, specified by the treatment planner to the IP computer system. The resultant inverse plan depends on the specification of these constraints. Overly relaxed or overly restrictive constraints may lead computer optimization to produce an inferior plan. In particular, if one specifies dose constraints that are physically impossible (for example, prescribing a 100% dose to a paraspinal tumor with a large penalty for underdose, plus a 5% dose to an adjacent section of spinal cord with a large penalty for overdose), the resultant inverse plan may be worse than a conventional plan. The reason for this seemingly incongruous result is that the optimization algorithm may attempt to adjust beamlet weights to extreme values in a futile attempt to meet dose constraints that are physically impossible.

To specify realistic dose constraints, users need to learn the relationship between the dose constraints and the resulting dose distributions for the specific IP system they are using, plus have some familiarity with basic radiation dosimetric concepts. This learning process is one of trial and error, and is less intuitive than the trial and error planning process involved in 3D-CRT planning. Once the treatment planner has gained this experience, it becomes possible to develop disease-specific templates that can be used as starting-point dose constraints for commonly treated tumor sites such as the prostate,^49,50 the nasopharynx,⁴⁵ and the oropharynx.⁵¹ Patient-to-patient anatomic variations obviously limit the use of standard dose constraints, but they can be good starting points.

Once the dose constraints are given to each type of tissue or organ, computer optimization alters the intensity of each beamlet until the OF (e.g., see equation) is minimized. The computational algorithms such as the downhill gradient method or the simulated annealing method are often used to expedite the search of the minimum.

Generation of Leaf Motion Files

The “leaf sequencer” is an algorithm within the IMRT planning system that translates the beam intensity pattern produced by the IP system into an instruction set describing how to move the MLC leaves during beam delivery. Depending on the specific IP system being used, the result of optimization may be either continuous 2-D intensity profiles, or discrete 2-D intensity matrices with discrete resolution, such as 10 mm × 1 mm spatially, with 5% intensity steps. The term DMLC delivery (often referred to as a sliding window) delivers a continuous 2-D intensity profile, whereas static delivery (often referred to as step and shoot) results in “discretized” intensity patterns.

There are many possible sequences of dynamic leaf motions (or equivalently, combinations of multiple static field segments) capable of producing a desired intensity pattern because the problem of leaf sequencing is underdetermined mathematically.¹⁵ Depending on the delivery method, algorithms have been developed to minimize the number of segments, the number of total MUs, the leaf travel distance, and the total delivery time.^52–57 Here, we describe one basic leaf sequencing method using DMLC (sliding window).

This leaf sequencing method⁵⁴ divides the 2-D intensity distributions into a number of one-dimensional intensity profiles, with each profile delivered by one pair of leaves. The leaf paths are illustrated in Fig. 10-8, which shows a schematic representation of radiation delivery using the sliding window method. The dotted lines represent the positions of a leaf pair (x-axis) as a function of beam-on time (y-axis). Both leaves start at the extreme left edge of the intended treatment field. As the beam is turned on (a), both leaves move at different speeds from left to right (initially the right leaf moves more rapidly than does the left leaf). The point P begins to receive radiation when the right leaf edge moves past it (b), and continues to receive radiation until the left leaf blocks the beam (c). By controlling the movement of the leaves and therefore the “beam-on-time” duration between b and c, one can deliver any desired intensity to point P, or any other point under this leaf pair. The solid line depicts the total integrated beam intensity to all points underneath the strip of tissue being treated by this leaf pair.

FIGURE 10-8 • Schematic representation of radiation delivery using the sliding window method. The dotted lines represent the positions of a leaf pair (x-axis) as a function of beam-on time (y-axis), and the solid line depicts the total integrated beam intensity to all points underneath the strip of tissue being treated by this leaf pair. See text for details.

By extending this concept to multiple pairs of leaves, any desired intensity-modulation pattern can be produced with designed sequences of leaf positions. Because each leaf pair must deliver a different intensity profile, the speed of each leaf and its position as a function of MUs delivered must be individually controlled. For maximum efficiency in beam delivery (i.e., shortest possible treatment times), it is necessary to be able to move all leaves simultaneously. This requires both leaf speed modulation and dose-rate modulation. Ideally, one would always treat at the maximum dose rate of the linear accelerator, but when a leaf is required to move a large distance, requiring a speed exceeding the maximum mechanical leaf speed, the dose rate must be reduced. In practice, not all desired intensity profiles are exactly achievable because of the constraints on leaf motion imposed by the design of the MLC and the clinical dose rate of the machine. The details of MLC design limitations on IMRT delivery are discussed more fully in “Delivery of Intensity-Modulated Treatment,” in this chapter.

Direct Aperture–Based Optimization

The typical pixel-based or fluence-based (described previously) IP optimization often results in complex IMRT plans, which prolongs the treatment time and dramatically increases the number of MUs by two to five times.⁵⁸ The latter could lead to increased leakage and scatter radiation through the MLC, and this indirect radiation contribution has been shown to adversely affect the accuracy of treatment delivery.⁵⁹ More importantly, the increased exposure from complex IMRT plans may also increase the frequency of radiation-induced secondary malignancies. It has been recently reported that the transition from 3D-CRT to IMRT resulted in a larger volume of normal tissue exposed to a low dose of radiation, which was estimated to increase the incidence of secondary cancers at 10 years from 1% to 1.75%.⁶⁰

The complexity of IMRT plans originates not only from the use of pixel-based or fluence-based optimization, but also from the separation of leaf sequencing from the optimization, referred to as a two-step process. During leaf sequencing, the computer program may generate many small area segments (or subfields) to avoid significant deviation from the initially optimized plan. The use of these small area segments in IMRT plans significantly reduces overall efficiency of radiation and may introduce additional dosimetric errors. Based on our local institutional plan acceptance criteria, this type of optimization typically results in a total number of segments ranging from 100 to 180 for head and neck IMRT plans, and a total number of segments ranging from 60 to 100 for prostate-only IMRT plans.

To simplify IMRT plans (or avoid overly complex IMRT plans) while improving delivery and radiation efficiency, many researchers have been working on increasing the efficiency of leaf sequencers.^{55,57,60–63} Some leaf sequencers minimize the total number of segments, whereas others minimize the total required MUs. Other researchers have proposed the use of smoothing filters to eliminate unnecessary noise inside the intensity profiles, either during optimization or prior to leaf sequencing.^64,65 With most commercial planning systems, options for controlling the complexity of an IMRT plan are often limited to choosing coarse intensity levels during conversion, selecting a leaf sequencer that can provide an optimal delivery efficiency for the specific delivery method (e.g., sliding window or step-and-shoot methods, discussed further in “Static Versus Dynamic Multileaf Collimator Delivery,” later in this chapter), or using smoothing filters.^66–68 However, the effectiveness of these methods in controlling the complexity of an IMRT plan is limited, often resulting in significant deteriorations in plan quality.

Recently proposed direct-aperture optimization (DAO) directly optimizes weights and leaf positions with a predetermined number of MLC shapes permitted for each beam.^69–72 DAO is a one-step optimization process that eliminates the step of leaf sequencing in two-step optimization and allows users to have direct control of the complexity of IMRT plans. In an initial study using their in-house software, Shepard and colleagues⁷¹ demonstrated the feasibility and advantages of using DAO on a variety of test cases. Compared with traditional two-step optimization, they found that DAO resulted in a significant reduction in both the number of segments and the number of MUs. They were able to create highly conformal IMRT plans with five or fewer apertures per beam angle. The following questions are often asked: Does this significant reduction in the number of variables in optimization lead to deterioration of plan quality? Or, for a specific disease site, how many segments are necessary, and is there a threshold?

Ludlum and Xia⁷³ systematically investigated the tradeoff between plan complexity and plan quality by comparing pixel-based two-step optimization and direct aperture–based one-step optimization. For both prostate and head and neck cancer, IMRT plans of various levels of complexity were generated using each of the optimization methods. For IMRT plans achieving a similar complexity level, as measured by the total treatment time, they reported that direct aperture–based one-step optimization resulted in improved plan quality, compared with pixel-based two-step optimization. Meeting the same plan acceptance criteria of IMRT plans, they concluded that direct aperture–based one-step optimization is an effective method for simplifying IMRT plans, reducing the probability for dosimetric errors and lengthy treatment delivery times.

Inverse Planning With Total-Variation Regularization

The advantage of the aperture-based optimization planning method is that the planner can control the complexity of a treatment. However, because the delivered dose depends on the nonlinear aperture shapes and the optimization problem becomes nonconvex, random search algorithms, such as simulated annealing or genetic algorithm, are commonly employed. The computation is intensive and requires tuning of multiple algorithm parameters in the searching and cooling schedules. Another disadvantage of segment-based treatment is that the number of segments for each field needs to be determined before the calculation, which reduces the degree of freedom of the decision variable space and compromises the optimality of the final solution. Zhu and Xing⁷⁴ have recently proposed an efficient total variation regularization (TVR)-based IP algorithm. Instead of directly applying the physical constraints of the apertures, they include a TVR term in the least square optimization to enforce the computed field intensity maps close to the deliverable intensities using a small number of segments. The algorithm is able to optimize the number of segments for each field based on the modulation complexity. The proposed algorithm requires only a simple adaptation of the traditional beamlet-based optimization, and thus has significant potential for improved IMRT. Mathematically, this optimization can be reformulated rigorously as a quadratic programming problem. Fig. 10-9 shows the actual fluence maps (or patterns) obtained using the TVR algorithm for the prostate patient. The resultant plan has different levels of modulation for different fields. It is seen that the TVR greatly reduces the complexity of the intensity map, and the resulting intensity map is close to a piecewise constant function. Fig. 10-10 shows the optimized beamlet intensity of the second field with and without the TVR. The resulting intensity map is close to a piecewise constant function.

FIGURE 10-9 • Fluence maps (or patterns) obtained using the total variation regularization algorithm for a prostate patient at beam angles of 35 degrees, 110 degrees, 180 degrees, 250 degrees, and 325 degrees.⁷⁴

FIGURE 10-10 • Optimized beamlet intensity of a field without (A) and with (B) the total variation regularization.⁷⁴

Volumetric Intensity-Modulated Arc Therapy and Rapid Arc

A volumetric tomotherapy system that can be delivered with a conventional linear accelerator has been proposed by Yu and colleagues,⁷⁵ and is often referred to as intensity-modulated arc therapy (IMAT). In this method, 2-D intensity-modulated beams at many equally spaced gantry angles are approximated by a superimposition of multiple shaped radiation fields with uniform intensity. The increments of intensity levels are delivered by individual arcs, with the leaf positions changing with gantry angle to produce the shaped fields. As has been pointed out, there are a large number of possible permutations of decomposition patterns for the 2-D beams, and therefore the design of the most efficient MLC position sequence in arc therapy is a challenging problem. In addition to the multiple rotation approach described previously, volumetric arc therapy can be implemented in a number of forms depending on the specific characteristics of the available hardware and software planning systems.^76–78 Arc-based radiotherapy modalities, such as conformal arc, RapidArc volumetric arc therapy, and volumetric intensity-modulated arc therapy (VMAT), have recently become commercially available for clinical use. These techniques are capable of delivering a therapeutic radiation dose to the tumor target while simultaneously moving the MLC leaves and the gantry. Both dose rate and gantry speed can vary in these systems, yielding a greater degree of freedom for generation of conformal dose distribution. For certain types of diseases, arc therapy demonstrated selected advantages over conventional IMRT. In addition to generating excellent dose distributions for deep-seated tumors, an important advantage of arc-based therapy is its fast delivery and reduction of MUs, which may significantly increase the clinical throughput and has the potential to reduce the probability of secondary cancer induced by increased total body dose.

The IMAT treatment plan for a given patient is usually obtained by approximating a continuous arc delivery with a large number of fixed-gantry beams, followed by optimizing an OF with respect to the weights and segment shapes of the beams. Although conceptually simple, the dose optimization for IMAT is quite challenging because of the enormous number of variables and the nonconvex feature of the problem. In the past few years, various stochastic algorithms have been applied to optimize the system. Most, if not all, algorithms are brute-force in nature without consideration of a priori knowledge about the angular space. Unless the number of trial-and-error iterations approaches infinity, it is generally not guaranteed that the global optimal solution will be found.

In reality, it is known that, for a given patient, some beam directions are better or worse than others in terms of the dose deliverable to the target volume, and this information can be used as prior knowledge to facilitate the IMAT IP process. Quantitatively, the angular space can be scored by using the so-called BEVD proposed by Pugachev et al.,^29–31 which ranks a beam orientation by what the beam could achieve dosimetrically without exceeding the dose constraints of the system. It is conceivable that the optimal weight of a beam in IMAT should correlate with the BEVD ranking. Ma and colleagues have recently proposed an IMAT IP algorithm with incorporation of BEVD information.⁷⁹ Instead of setting the initial weight of a beam arbitrarily, precomputed BEVD is adopted as the initial weight of the corresponding beam. By initializing the segment weights in an informative way, they showed that the convergence behavior and the final dose distribution of IMAT IP are significantly improved as compared with the conventional approach. Fig. 10-11 plots the OF versus the iteration step for different simulated annealing calculation methods for an IMAT pancreatic treatment. The BEVD-guided optimization significantly improves the computational efficiency and the plan quality.

FIGURE 10-11 • Plots of objective function versus iteration step for different simulated annealing calculation methods for a Volumetric Intensity-Modulated Arc Therapy pancreatic treatment.⁷⁹

Characteristics of Multileaf Collimators

To better understand some of the technical details of MLC-based IMRT delivery, we describe in this section the basic physical and geometric characteristics of MLC design, as implemented by three major manufacturing companies: Elekta, of Norcross, Georgia; Siemens Medical Systems, of Concord, California; and Varian Medical Systems, of Palo Alto, California. The three designs differ in many ways, including the location of the MLC relative to the conventional jaws, whether MLC leaves are single-focused or double-focused, the physical characteristics or shapes of leaves, leaf-movement restriction, and maximum achievable field sizes. These design features all affect IMRT beam delivery and dosimetry characteristics.

The MLC can be installed within the accelerator gantry head either above the upper set of secondary collimator jaws or below the lower set of secondary collimator jaws as a tertiary collimator. In the Elekta linear accelerator, for example, the MLC replaces the upper (Y) jaws (Fig. 10-12), although it is augmented with an additional 3-cm thick Y jaw to reduce the radiation leakage from the MLC. In the Siemens linear accelerator, the MLC completely replaces the lower (X) jaws (Fig. 10-13). In the Varian linear accelerator, the MLC is a tertiary collimator (Fig. 10-14) located below and in addition to the conventional X and Y secondary collimators. Placing the MLC below both sets of secondary collimators (as in the Varian design) places it closer to the patient, which, in principal, improves the geometric penumbra, but at the expense of a physically larger device with less clearance between the MLC and the patient. Placing the MLC above the secondary collimators (as in the Elekta design) produces a poorer geometric penumbra, but also results in a smaller and lighter MLC design with increased physical clearance between the accelerator head and the patient. The Siemens design obviously takes a middle course in this unavoidable tradeoff of geometric penumbra versus MLC size.

FIGURE 10-12 • A schematic drawing of the Elekta multileaf collimator.

(From Van Vyk J [ed]: The modern technology of radiation oncology, Madison, WI, 1999, Medical Physics Publishing.)

FIGURE 10-13 • A schematic drawing of the Siemens multileaf collimator.

(From Van Vyk J [ed]: The modern technology of radiation oncology, Madison, WI, 1999, Medical Physics Publishing.)

FIGURE 10-14 • A schematic drawing of the Varian multileaf collimator.

(From Van Vyk J [ed]: The modern technology of radiation oncology, Madison, WI, 1999, Medical Physics Publishing.)

Complicating design considerations is the fact that adjacent leaves must be able to slide smoothly across each other with minimal gaps between leaves to reduce radiation leakage and transmission. Most MLC designs accomplish this by cutting tongues and grooves into the sides of the leaves. The side of one leaf has an extended portion called the tongue, while the abutting side of the adjacent leaf has an indented portion called the groove. Two adjacent leaves are coupled together as the tongue of one leaf slides within the groove of the adjacent leaf. Fig. 10-15 shows schematically a “head-on” view of the MLC leaves, showing how adjacent leaves have interlocking tongues and grooves, plus the variations in transmission dose that result. Each leaf has a tongue on one side, and a groove on the opposite side. The manufacture of precise tongues and grooves into extremely small individual leaves is an exacting process that places a lower limit on the size of individual MLC leaves.

FIGURE 10-15 • Schematic “end-on” view of a multileaf collimator showing the tongue-and-groove design and the approximate resulting beam transmission.

Although this tongue-and-groove design reduces radiation leakage, it also complicates treatment planning dose calculations because the transmission through any leaf depends on whether the beam passes through the tongue, the center, or the groove portion of the leaf. The differences in beam transmission through tongues and grooves is demonstrated in Fig. 10-16, in which we show a sheet of radiographic film (center panel, Fig. 10-16B) that was doubly exposed with two matched MLC shapes, shown in Fig. 10-16A (left) and Fig. 10-16C (right), respectively. The two MLC shapes are complementary to each other, as suggested by Sykes and Williams.⁸³ That is, leaves open in Fig. 10-16A were closed in Fig. 10-16C, and vice versa. In Fig. 10-16B, underdose regions are clearly shown at the match lines where the tongue is exposed to radiation, demonstrating the tongue-and-groove effect. The magnitude of the dose reductions in these match line areas is approximately 17% and 25% for Siemens (MXE) and Varian (CL2300 C/D) linear accelerators, respectively, and similar results have been reported for the Philips (now Elekta) design.⁸³

FIGURE 10-16 • Dosimetric demonstration of tongue and groove effect using a doubly exposed film (B) with two matched multileaf collimator shapes, shown in (A) and (C), respectively.

Another important design consideration is focusing the ends of MLC leaves to match the radiation field beam divergence. This is, of course, a 2-D problem, as the beam diverges in both the direction along MLC leaf motion and in the direction perpendicular to MLC leaf motion (designated as the X and Y directions, respectively). Focusing MLC leaves in the Y direction can be accomplished in the same manner as in a conventional secondary collimator. The width of each MLC leaf is designed to be slightly narrower at the top of the leaf (nearer the radiation source) than at the bottom of the leaf (nearer the patient), as shown in Fig. 10-16. MLCs focused only in the Y direction are called single-focused MLCs.

Double-focused MLCs must also be focused in the X direction, which is parallel to the leaf motion. This is a more difficult problem because in the X direction the beam divergence is a function of the leaf position, and, to be perfectly focused in the X direction, the divergence angle at the end of each leaf must be equal to the arc tangent of the ratio of the leaf position divided by the distance of the MLC from the source. Of the three manufacturer designs discussed here, only the Siemens MLC is designed as a double-focused collimator (for all models released before 2008). This is achieved by having the leaves move along a spherical surface centered at the radiation source—a feat readily accomplished in the Siemens design because the MLC essentially replaces the secondary X collimator.

Both the Elekta and Varian MLCs (and a newer model released after 2008 from Siemens) are single-focused, with the leaf motion in a plane perpendicular to the beam direction rather than along a spherical surface, as in the Siemens design. This precludes the possibility of true 2-D focusing, but in the Varian design there is “pseudo” focusing in the X direction by machining the ends of the leaves not as flat surfaces but rather with a slight convex curvature⁸⁴ (Fig. 10-17). The convex leaf edge maintains a reasonably faithful geometric penumbra over all field sizes, but compromises slightly beam transmission through the leaf ends. As a result, the leakage depends somewhat on field size or leaf position from the beam center. Leakage can be as high as 20% or more⁵³ when opposing leaves abut each other (i.e., zero field size). Also, the coincidence between the radiation and light fields can depend on leaf position when using the rounded-leaf-end design.

FIGURE 10-17 • Schematic of a typical rounded-leaf-end design of a multileaf collimator. Note that the variable beam transmission affects the shape of the beam dose penumbra, and that both penumbra and coincidence of light radiation fields are function of leaf position.

The number of leaves and leaf thickness and width also vary between the different manufacturers and different models for the same manufacturer. Leaves are made of tungsten alloy, and the leaf thickness is often referred to as the physical thickness. The leaf width, however, is often referred to as the width projected at the isocenter (the physical dimensions of the leaves are, of course, less than half this size, depending on the physical location of the MLC from the radiation source). The maximum possible field size for a single IMRT treatment field is a function of the number of leaves, projected leaf width, and the distance that a leaf can travel past the field center (called the over-travel distance).

Leaf motion restriction is another factor affecting the delivery, efficiency, and dose accuracy of IMRT. Fig. 10-18 shows three types of leaf motion restrictions, in which all left bank leaves are denoted in gray and right bank leaves in black. Fig. 10-18A shows a design incorporating a minimum gap between opposing leaves (usually incorporated in the design to prevent leaves from hitting each other). Fig. 10-18B shows a design that prohibits interdigitation, which occurs when one (black) leaf end extends beyond the tips of its two neighboring opposing (gray) leaves. Fig. 10-18C shows a design restricting leaf over-travel, either beyond the field center, or beyond the back ends of its neighboring leaves.

FIGURE 10-18 • Three types of leaf motion constraints. (A) No leaf end can come closer than a fixed distance (typically 2-5 mm) to the tip of its directly opposing leaf or to the tips of its two neighboring opposing leaves. (B) A design that prohibits interdigitation. (C) Leaves are not allowed to extend either a fixed distance beyond the field center (typically 10-12.5 cm), or beyond the back end of the carriage (thus leaving an uncollimated rectangle of beam at the trailing edge of the leaf).

Static versus Dynamic Multileaf Collimator Delivery

As we have discussed, there are two conceptually different ways to deliver MLC-based IMRT: static delivery and dynamic delivery. However, one could consider DMLC-based IMRT to be a special type of static IMRT that uses an infinite number of continuously varying MLC shapes plus an infinite number of infinitely small incremental doses.

With SMLC-based IMRT, the desired intensity pattern is decomposed into a sequence of segments, each with uniform intensity. Such an intensity pattern can be created through manual FP or computer-optimized IP. The number of segments for a given intensity pattern depends on the complexity of the intensity pattern and the algorithm used for segmentation. One index that reflects the complexity of an intensity pattern is the number of intensity levels, similar to partial-thickness block transmission factors. The more intensity levels, the more complex the intensity pattern, and the larger the number of segments required to achieve the desired dose distribution.

In SMLC delivery, treatment parameters associated with each segment are recorded and verified through an R/V system. Autosequencing software is often used to automatically deliver each segment with little or no human intervention. The advantages of static, or step-and-shoot, IMRT are that it is conceptually simple, there are no requirements to control the individual leaf speeds (thus simplifying the MLC control system) or delivery dose rate, an interrupted treatment is easy to resume, it is easy to verify an intensity pattern for each field, and fewer MUs are required in comparison with DMLC delivery.⁸⁵ The disadvantage is that it may require prolonged treatment time, particularly for complex intensity patterns. The overhead time associated with downloading and comparing the parameters of each segment in the R/V system can take a few seconds. For a complex intensity-pattern delivery, this overhead time can be a significant portion of the total treatment time. Furthermore, because of the “discretized” intensity pattern, the achievable dose distribution for very complex clinical cases may be compromised.

In DMLC delivery, the leaves are in continuous motion during radiation delivery, as previously described in “Generation of Leaf Motion Files.” The advantage of DMLC delivery is that it can deliver a smoothly varying intensity profile. The disadvantages are that the delivery mechanism is rather complicated, involving leaf-speed and dose-rate modulation; it requires the precise control of individual leaf speeds; and small errors in the calibration of leaf position could (for very highly modulated fields) result in significant errors in delivered doses. Thus, very careful QA tests are required for dynamic IMRT. Finally, intensity patterns contain regions with zero dose intensity, such as for a cord block, requiring completely closed leaves to cross over this region. But with rounded leaf ends, the leakage through the closing leaf ends can be as much as 20%,⁵³ and some MLCs restrict the minimum leaf gap to several millimeters. In such a situation, one must either split the IMRT treatment field into two separate beam segments, or augment the IMRT treatment with the addition of a Cerrobend block.

Mechanical Tests

The geometric accuracy of leaf positions is critically important for both static and dynamic IMRT, much more so than are collimator settings for conventional radiotherapy. For dynamic IMRT, the dose delivered to each strip of tissue is directly related to the gap width of the leaf pair passing over it, and small errors in average gap width can result in significant dose errors. Consider, for example, an MLC field for which the average gap width is intended to be 1 cm, but for which one or the other leaves has a systematic error in position of 1 mm. The resulting gap, and consequently the delivered dose to that strip, would therefore be in error by 10% (i.e., 1 mm/1 cm).^84,88 Similarly, for static IMRT a systematic error in leaf position would affect the dose everywhere within the strip of tissue covered by that leaf, and not just at the field edge as it would for conventional static-beam delivery.

Thus, precise calibration and verification of leaf position is much more important for IMRT than for conventional therapy or for 3D-CRT. Leaf position is also somewhat more important for sliding-window than for step-and-shoot therapy.^89,90 Mechanical calibration of the leaf positions can be tested using procedures and software supplied by the manufacturers, although independent physical verification methods such as caliper and micrometer measurements of leaf positions are also recommended.^84,91,92 Several MLC test patterns have been designed and used for MLC leaf position QA. One such test pattern, shown in Fig. 10-19, is specifically designed to test for small errors in MLC leaf position. In this case, a leaf motion file is generated that instructs the MLC to deliver five narrow vertical bands of radiation. The leftmost panel shows a film exposure in which the MLC functioned properly, as indicated by the fact that all five dark strips are of equal intensity and thickness (lighter horizontal bands and streaks are the result of normal MLC leakage). In the rightmost panel, however, several individual leaves have been intentionally miscalibrated by −0.5, −0.2, +0.2, and +0.5 mm (top to bottom of film respectively). Note the narrow spots in all five vertical bands for the −0.5- and −0.2-mm errors in calibration, and the broad spots for the +0.5- and +0.2-mm errors. This demonstrates that a simple daily film QA test can detect MLC positioning errors of a fraction of a millimeter. A more elegant and quantitative method was proposed to use the fractional volume effect of a radiation detector or an electronic portal imaging device (EPID) for a routine leaf-position check.⁹³ The approach uses the fact that, when a finite-sized detector is placed under a leaf, the relative output of the detector depends on the relative fractional volume irradiated. A small error in leaf positioning changes the fractional volume irradiated and leads to a deviation of the relative output from the normal reading. For a given MLC and detector system, the relation between the relative output and the leaf displacement can be easily established through experimental measurements and used subsequently as a quantitative means for detecting possible leaf positional errors. Experiments using an ion chamber and an EPID showed that the technique is robust and can detect the positional inaccuracy of approximately 0.1 mm.

FIGURE 10-19 • Multileaf collimator film test pattern in which leaves are programmed to deliver five separate 5-mm-wide dose strips. Note that in the rightmost film certain leaf pairs were intentionally programmed to improper gap widths of ±0.2 and ±0.5 mm respectively, which is clearly visible on the films.

Another important consideration for DMLC is the monitoring of the performance of the MLC during treatment. While the beam is on, the Varian MLC control computer, for example, checks all leaf positions every 55 ms, compares them with the planned leaf positions in the DMLC file, and records them in a DMLC “log file.”^94–97 If any leaf deviates from its planned position beyond a preset tolerance, the control computer invokes a “beam hold-off,” and radiation delivery is withheld until all leaves are again within tolerance. Deviations that invoke “beam hold-off” should occur infrequently, as the leaf sequencer algorithm incorporates MLC leaf-speed limitations and accelerator dose rates when calculating DMLC files. Our experience has been that leaf deviations greater than 1 mm occur less than 1% of the time. Part of the DMLC control software permits the user to preset a tolerance level of leaf position error invoking a beam hold-off. It is found that 2 mm is a good compromise between ensuring against a true hardware failure (such as a “stuck” leaf) and an unreasonably small tolerance that would result in a large number of unnecessary beam hold-offs.⁸⁴

MLC leaf position accuracy and field penumbra become more important in IMRT treatment because they can affect dose throughout the entire field, not just at the edge of the field as in conventional treatment.^90,94–97 In static IMRT delivery, the IMRT field is essentially a series of abutted static fields. Random and systematic errors in leaf position may cause overlap or underlap in these abutted fields. It is found that leaf positional errors have more significant effects on the complex plans than on the simple IMRT plans.⁹⁰ Although the effect of random errors in leaf position are usually averaged out over the entire treatment volume, systematic leaf position error may be dosimetrically significant. An example of systematic leaf position errors is the coincidence between light and radiation fields for rounded MLC leaf ends, discussed previously in “Characteristics of Multileaf Collimators.”

Dosimetric Tests

Dosimetric characterization of the MLC, using film, ion chambers, or both, includes measurements of radiation transmission through the leaves and their rounded ends, and the determination of head scatter.^56,98 The dosimetric contribution of these factors can amount to as much as 15% for large, highly modulated fields. Periodic dosimetric and geometric verification of intensity-modulated fields must therefore be added to the physics QA program. For example, image patterns of predesigned fields can be used to expose radiographic films on a regular basis by the radiation therapists to provide a quick visual assessment that the MLC is functioning properly. Ion chamber and diode array measurements at different gantry and collimator angles are performed monthly by a physicist to ensure constancy of the MLC output and to track long-term stability. Ion chamber measurements in a solid phantom for patient fields provide a straightforward check on the MU calculations. Film dosimetry, with sufficient spatial resolution for the intensity-modulated patterns, efficiently compares the delivered and the planned dose distributions. It is also used intensively during the testing of new software and new treatment sites for IMRT, as well as for periodic spot checks.

Additional dosimetry tests related to dose linearity plus field symmetry and flatness are required for IMRT, particularly for the step-and-shoot technique. The step-and-shoot IMRT technique often introduces delivery of many small dose segments, sometimes as low as a fraction of one MU. In such situations, the normal internal accelerator feedback dosimetry systems that ensure beam symmetry and flatness may not function properly, and special dosimetry tests need to be performed for this purpose. In particular, dose linearity as a function of MU setting, plus flatness and symmetry need to be checked for very small MU settings.^99–103

In conventional delivery, a broad and uniform x-ray beam is desirable and obtained through a flattening filter in most linear accelerators. The beam flatness and symmetry are monitored by multiple internal ionization chambers, and the flatness and symmetry are further adjusted through feedback circuits in the linear accelerator. This feedback mechanism works well in conventional treatment delivery, but for the small number of MUs typically used for IMRT segments, the operation of this feedback circuit mechanism should be specifically verified. In conventional delivery, the beam flatness and symmetry are checked by the analysis of beam profiles measured at various field sizes. In the static IMRT delivery, the field symmetry and flatness can be checked with ionization chamber array or films.

Patient-Specific Issues

Finally, specific tests should be performed to ensure the accuracy of each patient IMRT treatment field. This includes verification that all field and file names are correct, and that the proper DMLC leaf motion files have been downloaded to the linear accelerator through the R/V system. For dynamic IMRT, both the outer boundary of the treatment field plus the intensity pattern should be verified prior to the first patient treatment. One technique for performing this test is to attach a sheet of radiographic film to the blocking tray of the accelerator, expose it to an entire fraction of a dose, and compare the developed film with the expected intensity pattern. This exposed intensity pattern is the initial beam fluence pattern (i.e., without patient attenuation), and should match that calculated by the treatment planning system. An example of such a test is shown in Fig. 10-20, which demonstrates that a simple visual inspection can easily reveal gross errors in treatment delivery or field identification. More accurate comparisons could of course be done by scanning the film for true optical density and dose.

FIGURE 10-20 • An example of a verified intensity pattern, compared with the intensity pattern from the treatment planning system.

As discussed earlier, verification of absolute dose to specific points within each IMRT field must also be performed. If one does not have an independent computer program to perform these calculations, then in-phantom dosimetry measurements using an ionization chamber, diodes, thermoluminescent dosimeters (TLDs), a diode array, or film must be made. This requires the creation of a new treatment plan, or at least dose calculations at a few specific points in phantom for the exact intensity patterns from each patient treatment field, a feature that most IMRT treatment planning systems have. These measurements are best made in regions with relatively uniform doses to prevent apparent errors caused by small displacements in dosimeter position. For film measurements of IMRT fields, one should be particularly cognizant of possible film calibration errors caused by the large amounts of low-energy-scatter radiation that are present in large, highly modulated IMRT fields (radiographic film over-responds to low-energy x-rays because of its high silver content). Finally, in vivo dosimetry measurements can be made in the patient using diodes or TLDs, but again measurements should be made in regions with a relatively uniform dose.

Computerized Second Check of Intensity-Modulated Radiation Therapy Plans

Verification of IMRT treatment plans is currently a labor-intensive and often institution-dependent process. The use of independent MU and fluence map calculations for additional assurance of IMRT delivery have been reported in the literature.^104–106 The computer-based approach has a number of advantages. Instead of using an ionization chamber or radiographic films to actually perform the dosimetric measurements, it attempts to simulate the treatment on a computer and independently calculate the dosimetric quantities, such as the spatial doses or fluence maps. The philosophy here is similar to that used in plan validation in conformal 3D-CRT, in which a manual or simple computer calculation is employed to double-check the MU of a treatment.

A general algorithm developed by the Stanford group¹⁰⁵ provides a clear physical picture and allows implementation of the MU calculation at different levels of sophistication to meet the specific requirement of different systems. In the independent fluence map calculation, the program reads in the leaf sequence file generated by the planning system and recalculates the fluence map. The calculation is then compared quantitatively with the intended fluence map from the treatment planning system. The goal of the simulation is to warrant that, assuming that a rigorous independent QA of the MLC system has been performed so that the DMLC can accurately execute the instruction of a leaf sequence file, the execution of the leaf sequence will generate the desired fluence map, should it pass the simulation test. This eliminates the experimental verification for each treatment field and each patient and significantly simplifies the QA procedure.