Recent advances in radiotherapy treatment planning.

Cancer Investigation, 9(4), 465-481 (1991)

SPECIAL ARTICLE Eli Glatstein, M.D., Editor for Radiation Therapy Series

Cancer Invest Downloaded from informahealthcare.com by Mcgill University on 11/03/14 For personal use only.

Recent Advances in Radiotherapy Treatment Planning Julian Rosenman, M.D., Ph.D.,t Edward 1.Chaney, Ph.D.,* Scott Sailer, M.D.,* George W. Sherouse, M.S.,* and Joel E. Tepper, M.D.* Departments of *Radiation Oncology and tComputer Science University of North Carolina at Chapel Hill Chapel Hill, North Carolina 27599

ABSTRACT

Radiation treatment planning is currently in a state of rapid change. Dissatisfaction with past planning technology s t e m from the growing realization that: (1)Increases in the local regional tumor control rate will increase the cure rate in many malignancies. (2) Even at the best treatment centers geometric tumor misses are commonplace. (3) Traditional constraints on treatment techniques, originally imposed for simplicit y and reproducibility, are no longer necessary, and can result in suboptimal treatment. (4) Treatment plans judged “optimal” in two dimensions may be farfrom optimal when viewed over the entire treatment volume. (5) Luck of treatment reproducibility is also commonplace, and can be demonstrated to adversely affect treatment outcome. On the positive side, recent developments in computer graphics, image processing, radiationphysics, and radiation biology are now making it possible to deJine, design, and deliver sophisticated 30 radiation treatments. However, because many of these technologies are being developed for other disciplines, their applicability to radiation therapy treatment planning is not widely appreciated. We outline the current status and new developments in radiation therapy treatment planning.

465 Copyright

0 1991 by Marcel Dekker, Inc.

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INTRODUCTION Radiation portal design was done originally from external patient anatomic landmarks with the occasional aid of a plane radiograph. Doses were hand calculated, so that the number of alternative treatment plans that could be produced for clinical consideration were extremely limited. With the advent of the radiation simulator it became possible to design radiation portals directly from plane radiographs which made tumor targeting and protection of dose-sensitive normal tissue far more accurate. CT scans and other 3D data sets could be used to increase the information content of the simulator films, much as the plane radiograph had aided earlier clinicians. Radiation dose calculationsbecame computerized and it became possible for the clinician to take into consideration the entire planar dose distributions, not just the dose at a few select points. Additionally, alternative treatment plans could be generated all0 wing the radiation oncologist greater flexibility in choosing the treatment technique. Recent advances have now made it possible to bypass the plane radiograph and design the treatment plan directly from the much richer CT data set. However, this transition has not been easy, and many significant problems have yet to be solved. We have been involved in researching 3D treatment planning at the University of North Carolina since 1983. An exciting aspect of this work is that many of the techniques needed for 3D planning already exist in other fields. To take advantage of this knowledge we have combined our efforts with researchers from such diverse fields as 3D computer graphics and image processing, perceptual psychology, pure mathematics, human vision, surgery, radiology, biostatistics, and anatomy. We review recent advances in radiation treatment planning from the perspective we have taken at the University of North Carolina, as an example of the type of development being done here and elsewhere.

LIMITATIONS OF CURRENT TREATMENT PLANNING TECHNIQUES Tumor Coverage The radiation therapy community has long accepted the fact that adequate tumor coverage requires generous radiation fields (1,2). However, even with carefully designed radiation fields, geometric accuracy (covering the entire tumor volume with all radiation beams) is frequently not achieved. Reviewing six published reports, Tepper and

Padikal (3) found that treatment planning without CT resulted in inadequate tumor coverage in 195/402 (49%) patients when the final plans were checked with CT. In a more prospective manner, Lichter at the University of Michigan found that CT-based treatment planning resulted in treatment changes in at least 40% of all patients conventionally planned (Lichter, personal communication). The importance of improving local-regional tumor control rates is shown in Table 1 (4-6). For these tumors one could, perhaps, increase cure rates by as much as 10% if the local tumor control were 100%. Of course, adequate tumor coverage per se cannot insure local tumor control, but Goitein (7) has estimated that the elimination of geometric tumor misses alone might increase cure rates by 3-4%. On the other hand, the estimates of localregional failure rates in Table 1 may be conservative. For example, in the RTOG randomized trial #73-01 for treatment of inoperable, non-small-cell lung cancer (8), the local failure rate determined only by noninvasive techniques was 35 % for the best treatment arm; the pathologic failure rate may have been much higher. Finally, even if sterilization of local regional disease does not lead to cure (because of metastatic disease) it remains a worthwhile goal because it can substantiallyimprove a patient’s quality of life.

Radiation Beam Placement Radiation beams are traditionally oriented in a few standard configurations for simplicity, for ease of understanding the anatomy through which the beams pass, and to give a reasonable tumor-dose conformation (tightness of fit between the tumor and the dose distribution). For brain and head and neck tumors, this has meant use of opposed lateral fields; for the lung, opposed anterior-posterior fields (AP-PA); and for pelvic tumors, a combination of laterals and AP-PA, the “four field box. The only common variations are the occasional oblique fields used in off-cord lung boosts, tangent fields for breast cancer, and wedge pairs sometimes used in the head and neck region. Even the wedge pair technique is often avoided, as many regard it as too complex to allow for field shaping (9). Beam placement out of the transverse plane is rarely used, and the use of non-coplanar beams (beams whose central rays do not lie in a common plane) is rarer still (10). On the other hand, tumors are usually complex 3D structures, often of great irregularity. As Lichter points out: “In radiation therapy treatment planning one has always had to assume. ..that tumors fit into relatively uniform volumes such as boxes or cylinders” (11). Can ”

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Table 1

Estimated Increase in Survival If All Local-Regional Failures Could be Eliminated


Malignancy

New cases per year

NO. of localregional failures

Additional no. of cures if there were no local failures

-

Uterine cervix (4) Oropharynx (4) Colo-rectum (4) Ovary (4) Lung (non-oat cell) (5) Prostate (5) Bladder (5) Soft tissue sarcoma (6)

16,000 18,700 120,000 18,000 102 ,000 75 ,000 38,500 4,250

3,680 5,050 36,000 7,200 20,400 9,750 11,935 1,275

2,700 2,000 17,600

Total

392,450

95,290

38,000

the use of alternative treatment plans be justified in terms of time and expense? The answer to this question depends on demonstrating the following: (1) That alternative plans can lead to a tighter fit between tumor and the spatial dose distribution (better tumor -dose conformation); (2) that better tumor-dose conformation will allow higher tumor doses to be delivered at the same level o f morbidity than is possible with standard treatment plans; and (3) that higher tumor doses result in better local-regional tumor control.

Treatment Plan Evaluation In two-dimensional planning the experienced clinician usually can decide quickly which of several competing treatment plans is best. Consider the treatment plan for prostate cancer shown in Figures 1 and 2. In Figure 1 the fields are equally weighted, in Figure 2, a 1.3: 1, lateral to AP-PA weighting is used. The major difference in dose distribution is that in the first plan the tissue anterior to the tumor is more heavily dosed, in the second, the femoral heads are more heavily dosed. Thus the decision as to which plan is better is reduced to a single welldefined medical judgment. Alternative beam arrangements could be considered (e.g., Fig. 3), but most clinicians use a “generic” beam arrangement for the majority of patients, as alternative 2D plans usually offer no clear cut advantage. The dose calculation, often considered synonymous with treatment planning, then serves only to determine the field weights and wedges; a “fine tuning” of the treatment plan. As opposed to 2D, the criteria by which a 3D treatment plan can be judged are not currently well understood, and for many situations may be very complex. For

2,000 7,200 3,600 3,000 900

example, it would be hard to choose between a treatment plan for lung cancer that irradiated 70% of the normal lung to a dose of 500 cGy, and one which irradiated only 30% of the normal lung, but to a dose of 2500 cGy. One cannot reduce this question to well-defined medical judgment until the normal tissue complication probabilities (NTCP) of the lung (and other tissues in the beam paths) can be estimated for each case. Unfortunately, the lack of reliable clinical data on the response of organs and tissues to inhomogeneous doses of radiation makes it very difficult to calculate any NTCP with confidence. Ironically, this data can only be accumulated from studying the records of patients who have already undergone 3D treatment planning. In addition to the lack of proven methods to objectively score 3D radiation treatment plans, suitable methods to display 3D dose distributions superimposed on the relevant anatomy are also lacking. Thus to subjectively score a given 3D radiation plan, the radiation oncologist must view the radiation isodoses superimposed on most or all of the CT slices (typically more than 50). Experience has shown that under these conditions it is not always easy either to recognize the best treatment plan, or to suggest useful modifications (12).

Reproducibility of Treatment Reproducibility of treatment (quality control) is not traditionally considered to be part of treatment planning. However, it has been demonstrated that the lack of treatment precision can have a severely adverse effect on patients with cancer of the nasopharynx (13), lung (14-16), and Hodgkh’s disease (17,18), and probably many others. Since the theoretical advantage of any treatment plan can

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Isodoses shown:

#)

50 70

90 95

95


= -

Femoral Heads

2‘ Figure 1. A standard “four-field box” arrangement for the treatment of prostate cancer. The fields are weighted evenly.

be offset by inadequate precision in treatment, methods of ensuring, and monitoring treatment reproducibility must be well formulated and included as an integral part of treatment planning. Lack of treatment precision can be due to many problems, including lack of sufficient patient immobilization, improper transfer of setup instructions from the simulator to treatment machine, and treatment setup error. There is some evidence that lack of proper patient immobilization can result in routine positioning errors of 1 cm in some clinical situations (19). Because some alternative 3D treatment plans may require more precise setups than traditional plans, and especially because one must be able to accurately register CT scans with the patient, better patient immobilizationmethods may have to be developed (20).

Radiation portal films are currently the most important mechanism for assuring quality assurance of radiation therapy delivery (2 1,22). Unfortunately, high energy xray beams intrinsically produce low contrast films due to the Z-independence of Compton interactions (23). Portal films often vary widely in quality (24), making consistent interpretations difficult. There have been many attempts to improve the readability of portal films. The simplest of these is to introduce high contrast markers into the patient such as mercury in the bladder, gold seeds into tumor beds, or lead solder around palpable lymph nodes. It is almost universal practice to employ some form of metal screen in the film cassette to improve contrast (25). Finally, carefully choosing the x-ray film can have an important effect for high-energy imaging (Hutchins, internal unpublished

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Isodoses shown: 20 50

70 90


96 98

Figure 2. The same treatment plan as Figure 1 except the fields are weighted 1.31, laterals to AP-PA.

report). More ambitious efforts to improve the quality of portal films have utilized light-emitting screens (26), xeroradiography (27,28), and successive contact printing of radiographs (gamma multiplication) (29). None of these more sophisticated approaches has enjoyed widespread success because the modest improvement in film quality that they offer does not seem to justify the additional cost of hardware, materials, and labor. Another approach to the improvement of portal film quality has been to mount a diagnostic x-ray machine on the therapy machine gantry so as to take diagnostic quality verification films (30). This approach has also never been widely employed, perhaps because of the expense and clumsiness. The diagnostic head and counter weight tend

to restrict gantry movement, collimator rotation, and the use of standard blocks and wedges (30). Also the time to record a portal film and reset the machine is increased. Finally, although the diagnostic head can be adjusted to match the treatment field within one millimeter, the potential for serious error always existed because one is never directly viewing the treated field. Dosimetry At present, most radiotherapy photon treatment plans consist of two dimension dose distributions such as shown in Figures 1 to 3. The patient model is taken as a homogeneous water density slab, extending indefinitely along

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Isodoses shown: 50 70

90 %


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Figure 3. A “four-field diamond” arrangement for the treatment of prostate cancer. The fields are weighted evenly.

the z-axis. All of these methods suffer from a number of clinical limitations: 1. Patient tissue inhomogeneities are often not considered. For example, in the area of greatest tissue inhomogeneity, the lungs, clinical doses are usually prescribed without lung corrections (15). However, it has recently been shown that doses as high as 7000 cGy may prolong survival in some lung cancer patients (31) and as a 5 % increase in lung dose can produce a 12 % increase in the incidence of acute pulmonary damage (32), lung corrections may become very important. 2. Doses near shielding blocks are not routinely calculated accurately. For example, in cases with great tissue depth, as in lateral pelvic fields, the distance from a sacral block to the 95 % isodose line may be greater than

1 cm (33). The biologic dose reduction is even greater in that both the total dose and the dose per fraction near the field edge is reduced. 3. Doses in the “buildup” region and other areas that lack electronic equilibrium are difficult to calculate (34). Currently these must be estimated from standard charts which do not take into account all the individual patient variables, or by use of in vivo TLD measurements. 4. Dose calculation times are too long. Two-dimensional dose calculations typically require a few seconds to minutes for the four-field pelvis plan in Figures 1 to 3. This is rapid enough so that several weighting and wedge variations can be explored. However, the same plan in 3D, using a grid of at least 100,000 points, may require a half an hour or more, even on a relatively fast

Recent Advances in Radiotherapy Treatment Planning workstation. And in 3D, many more reasonable beam configurations are possible than in 2D so a greater number of plans will often have to be calculated.

ADVANCES IN RADIATION THERAPY TREATMENT PLANNING


CT-Based Treatment Planning to Increase Tumor Targeting Accuracy There is little doubt that CT scanning has improved the accuracy of tumor targeting for nearly every treatment area (35). At first, CT scans were used only as a supplement to regular treatment-planning procedures, and it is still standard practice to manually transcribe the tumor or target volume from a CT scan onto a simulation film. However, it is also generally recognized that such methods are only approximate and are subject to substantial error. The first practical approach to more tightly integrate CT scans into the treatment planning process was made necessary by the advent of particle beam therapy, especially proton therapy. Treatment with protons appears to offer no biological advantage over conventional x-radiation; their value lies in the physical dose distribution that can be achieved because of the very sharp proton beam edge, and the Bragg peak. To take advantage of proton therapy, therefore, one must develop methods that permit very accurate tumor targeting. Goitein et al. (36) have described these techniques in detail. Their chief treatment planning successes have been the development of methods to use CT in 3D treatment planning, to compensate and shape the dose distribution in three dimensions, and to precisely reproduce the treatment from day-to-day with careful patient immobilization and radiographic alignment in each treatment (36,37). There is now definite clinical evidence that these advanced treatment plans have made a difference in patients with tumors that lie near to dose critical structures (38). There has also been considerable effort at a number of institutions to develop CT based treatment planning systems for photons (11,39-49). Listed below is a synthesis of the most important features developed by these institutions. The first step in all these systems is to procure a high quality CT scan with the patient in the treatment position. The relevant anatomic and tumor-related structures on the CT scans must then be identified and contoured. Abstraction of these essential structures from typically 50-80 CT slices (image segmentation) requires many physicianhours to accomplish if done manually. Automatic con-

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touring programs based on edge detection have been developed (50) but objects with weak or incomplete edges must still be contoured by hand. Attempts have been made to strengthen weakly contrasted objects with various image processing techniques (51), but the results have not been satisfactory. Other approaches include the use of artijcial intelligence (AI) techniques to compare images to a “digitized atlas” of normal anatomy (52). Perhaps the most promising research areas in automatic image segmentation is the use of multiscale resolution methods that attempt to model human vision (53). Coggins has recently reviewed automatic segmentation methods and has concluded that much research remains to be done before such methods are practical in the medical field (54). Once the relevant anatomic structures, and tumor and target volumes are defined the treatment technique must then be determined. In its most pedestrian form, the treatment planning can proceed in a traditional fashion with the radiation portal being designed on simulation films. The previously defined anatomic and tumor data can then be automatically projected onto the simulation films in several fashions. This simplest way to do this is to plot the desired data scaled to the physical size of the simulation film. The plot can then be placed behind the film and the tumor and/or anatomy manually traced onto the film. A more sophisticated approach is through the digitally reconstructed radiograph (DRR) (42, 5 5 ) . This is a technique whereby a place radiograph is calculated from the CT data by casting rays through the CT stack and computing each radiographic path length. Tumor, anatomy and the simulator grid marks can also be projected onto the film (see Fig. 4). A printout of the DRR, which contains the tumor and relevant anatomy can then be used to draw shielding blocks. The most obvious advantage of this technique is its simplicity, for the clinician does not have to learn any new treatment planning skills. Additionally, the clinician does not have to spend anymore time than would be necessary to conventionally plan a patient. Although the treatment planning method described above has the virtue of simplicity it does not take full advantage of the 3D data set generated by CT scanning. As a result altenative plans that overcome the limitations of the radiation therapy simulator cannot be considered. A much more innovative approach has been advocated by Sherouse et al. (56), who have created a software simulation of a radiation simular (the virtual simulator). When using the virtual simulator, the abstracted anatomy and tumor are displayed as a three-dimensional object that can be freely rotated and manipulated in near-real time. Radiation beams can be placed at any arbitrary angle or

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computed tomography (SPECT), and more traditional nuclear medicine and ultrasound studies can often image physiologic or chemical changes due to tumor and thus may substantially improve tumor localization (40). However, correlating tumor information and patient anatomy in the supplemental images with the planning scans is complicated by a number of factors including: (i) differences in patient positioning; (ii) patient motion such as shifting and deformation of internal anatomy; (iii) imager artifacts, e.g., spatial nonuniformities that warp the image space; and (iv) fundamental differences in image creation. One widely explored strategy for deriving that transformation matrix is to optimize the fit of one patient representation to another by minimizing a mismatch function based either on fiducial points (57) or surface fitting (58-60). None of these techniques is yet precise enough for routine clinical use and the automatic registration of alternative imaging modalities remains at the basic research level. Thus the clinician wishing to use alternative imaging techniquesmust still transfer them by hand to the CT scan.

Treatment Plan Optimization: An Overview Figure 4.

A digitally reconstructed radiograph from a CT scan of a patient’s chest. Shown are the contours outlining the trachea and bronchi.

isocenter allowing a full range of exploration. When a beam configurationis chosen, a DRR is computed for each beam along with a set of directions for setting up each beam. The patient then undergoes a conventional simulation to verify that the physical radiograph is aligned with the computed one. Virtual simulation, along with the initial clinical results has been extensively described elsewhere (49). Treatment planning with this approach is now in use in several radiation oncology departments.

The Use of Imaging Modalities Other Than CT for Treatment Planning CT scans provide the most accurate anatomic representation of the patient available at present. However, given the diversity of information needed for optimal treatment of cancer, no single imaging modality can produce a study that contains all possible data that might be relevant to the design of radiotherapy portals. Imaging technologies such as magnetic resonance imaging (MRI), positron emission tomography (PET), single photon emission

The goal of the radiation treatment planner is to find the “best” treatment plan for a given set of real world constraints. Traditionally this has not been a difficult problem because treatment planning has been limited to 2D considerations, and practical restraints have substantially reduced the type and number of treatment plans that could be considered. When some of these constraints are lifted, the task of producing (or even recognizing) a near optimal plan becomes substantially more difficult because of the large “search space” available to the treatment planner. For example, from 1-8 (or more) static radiation beams could be used, and these could be positioned at arbitrary orientations. Arbitrary beam modulators including wedges, beam weighting, and tissue compensators could also be added. Dynamic therapies are possible. These may be limited to gantry rotation during therapy (simple arcs), but may also include dynamic field shaping as well (dynamic conformational therapy) (61). Even more exotic possibilities abound. As Smith et al. have pointed out: “In the most general case the beam source would be free to move anywhere in space outside the patient; all possible parameter changes could occur in a few seconds; and the therapy beam could have any desired shape at any moment during the treatment” (62). Although this ideal cannot be achieved the following parameters might be changed (62):



1. Gantry position 2. Collimator orientation 3. Field size and shape 4. Wedge position and angle 5 . Table tilt, rotation, and 3D translation 6 . Dose rate 7. Photon-electron mode 8. Photon-electron energy

Since even the number of implementable static treatment plans is far too large to be individually reviewed some method must be found to eliminate the vast majority of those that are ill-suited. Typically the radiotherapist with a consultant (e.g., a physicist or dosimetrist) narrows the search range to a manageable few plans, and then relies primarily on his or her own store of expert knowledge to help iterate toward an “optimal” plan. As discussed, this practice may be effective for 2D planning, but probably will be ineffective for true 3D planning. More powerful methods, then, are needed to objectively and subjectively score 3D radiation treatment plans. These will be discussed in detail later.

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planes (63), the incident beam angle (and thus the oblique planes in which to calculate the scans) is what needs to be determined. It is likely, therefore, that the use of traditionally formatted CT data alone would lead the treatment planner to use only transverse coplanar beams. For these reasons, a 3D display is necessary to plan 3D beam arrangements. Currently, 3D radiation treatment planning is done on simple wireloop displays. These can be quickly calculated and rapidly manipulated, even on modest computing equipment. However, wireloops cannot show many of the anatomic landmarks useful for treatment planning, and are not useful for portraying 3D dose distributions. For these tasks, higher quality 3D displays are needed. Until recently, high-quality portrayal of 3D anatomical structures from CT or MRI data sets could be obtained using a binary surface-based technique. All CTpixels (picture elements) are classified as to whether or not they are part of a given surface. This surface is then modeled by many small polygons, and reflections of ambient light (64), shadows, textures, and other effects were added to enhance the 3D effect. Figure 5 is an example of a display

Radiation Beam Placement Current approaches to providing at least a first guess at an optimal treatment plan fall into three general categories: a system to allow human beings to manually explore multiple beam arrangements, “algebraic” methods that allow a computer to rapidly explore thousands of treatment possibilities and pick the best one based on a very simple scoring method, and artificial intelligence (AI) methods wherein the computer uses a knowledge base and a set of logic rules to deduce an optimal beam arrangement. None of these methods requires explicit dose calculations. Manual Methods If a human being is to be responsible for finding a satisfactory beam arrangement, it is of great importance that the patient’s anatomy be displayed so that it can be quickly and easily determined what lies within the beam path and what will be excluded. Additionally, the display should provide strong clues as to which beam orientations are likely to be worth considering. CT slices provide a satisfactory but inefficient visualization for solving the first problem, but can only be used to place beams in the transverse plane, as nontransverse planar beams often have a highly irregular and nonintuitive project on transverse CT slices. Although methods exist to rapidly calculate the appearance of CT slices in arbitrary oblique

Figure 5. Shown is the chest of a woman with a cancer in the left upper lung. Since these displays can now be made fully interactive one could manipulate this display and experiment with multiple incident beam angles.

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that might be of use in tumor targeting. Shown is the chest of a woman with an obvious cancer in the left upper lung. Since such displays can now be made fully interactive (65), one could imagine manipulating this display until, for example, the radiation beam intersected only a minimum amount of the opposite lung. The feasibility of using surface-based rendering for tumor targeting and for display of radiation isodose surfaces has been discussed by Rosenman et al. (66,67) for external beam, and by Chaney et al. (68) for brachytherapy. Despite the high quality of presentation and the evident usefulness provided by the surface-based display technique, it suffers from a serious weakness-the objects of interest must be defined in a binary fashion before rendering can begin. In other words, the anatomy and tumor must be recognized and understood prior to display in 3D form, and thus the displays may reflect the bias of the treatment planner. Recently a new display approach has been developed known as volume rendering, or volume compositing, which is done entirely without binary decisions (69,70). In Levoy’s technique (69) both a grey-scale shade and partial opacity is computed for each voxel in the data set, based on its surjiuce likelihood. These values are then blended along the viewing rays to form a translucent gellike volume image. Figure 6 shows the higher quality rendering that can be achieved by using this technique. Volume rendering as described above has two theoretical advantages over methods based on binary surface specification. First, minimal predefinition is required. Ultimately it may be sufficient for the user to merely specify regions of interest to exclude objects likely to obscure or confuse the portrayal of objects of interest. It is also likely (although not yet convincingly demonstrated) that volume rendering can effectively portray soft tissues in addition to bone. Second, the presentation of the objects is improved, since the method takes into account all the image intensities in a surface region rather than being limiting to binarily chosen surface points. As a result, volume rendered images m y be less prone to false positives (spurious objects) or false negatives (missing objects). Volume rendering, and other 3D visualizations are areas of active investigation in computer graphics. A combination of a volume rendered image and a digitially reconstructed radiograph with overlaid anatomy and tumor might provide the most powerful visualization yet for aiding in beam placement. The DRR is a familiar and superb method to quickly determine what structures lie inside and outside of the radiation field, and the volume rendered image offers powerful clues as to which beam

Figure 6. A high-quality volume rendering of a patients head using the Levoy technique. Volume rendering may ultimately prove useful for tumor targeting and radiation display provided that it can be made fully interactive.

orientations should be considered. Unfortunately, it is not yet possible to compute either of these images rapidly enough to allow for manipulation of the radiation beams in realtime. However, special purpose computing equipment may allow for this in a few years (71).

Algebraic Computer-Based Methods Computer-based methods for finding optimal beam arrangements have several advantages over using human beings in that a vast number of possibilitiescan be considered and consistent scoring criteria can be objectively applied. For example, one program examined plans at the rate of 200,000 per hour (72). Limited and somewhat arbitrary guidelines define how the computer selects and changes the many parameters that determine the shapes of the isodose distributions. Unfortunately it is possible, and in fact likely that substantially different plans will score within the acceptable range, requiring additional and more restrictive analytical tests. Recent work has generalized the scoring function to incorporate estimates of outcome based on probabilities of tumor control and/or complications (73-75). Statistical decision theory is then used to derive a utility function to score trial plans. A direct search algorithm is used to

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Recent Advances in Radiotherapy Treatment Planning seek out treatment setups that will maximize the utility function. Although the program is designed to run without human intervention, it is possible for the radiotherapist to impose certain constraints above and beyond the factors considered by the utility function. Constraining methods attempt to express rules as mathematical constraints, for example, dose limitations imposed at points or sets of points in the irradiated volume. Linear or quadratic programming techniques, for example, are used to discover treatment setups resulting in isodose distributions satisfying the constraints (76,77). The plans are prescored and only those potentially suitable are computed. A number of such plans can result; improvements in potentially acceptable plans can be made by modifying the constraints and recomputing. Recent investigators have attempted to make the constraints as comprehensive as possible by specifying a complete dose distribution (78,79). Major problems with all these algebraic approaches include: (i) uncertainties associated with the probabilities assigned to important outcomes such as complications and tumor control, (ii) accounting for all treatment variables such as fractionation and concomitant therapies, (iii) estimating the effects of partial and/or inhomogeneous irradiation of tumor or sensitive organs, (iv) the need for complex and computationally intense search strategies imposed by the multidimensional parameter space over which the search must occur, and (v) the necessity of arbitrary decisions to select where the search starts and in what direction it progresses. Perhaps one of the greatest shortcomings lies in the narrow and incomplete specification of optimization criteria embodied in the scoring functions and constraints. Even when an “optimal” plan is automatically derived, most likely the plan is optimal only within very narrow limits, since the criteria cannot characterize a sizeable fund of clinical knowlege gained from years of experience. This fund of knowledge is difficult to quantitate and is best expressed in the exercise of clinical judgement on a caseby-case basis. Expert (artificial intelligence) systems, however, may be able to predict clinician judgement with sufficient fidelity to be of clinical usefulness in certain situations, assuming that the database is free from unwanted bias.

Artificial Intelligence Methods Very little has been done on the application of expert systems to radiotherapy problems. Altschuler and Whittington (80) describe an interactive statistical package that learns the decisions of an expert from random cases well

enough to predict the judgement of the expert in new cases. Application of the computer-based expert system to predicting the treatment of prostate cancer showed excellent agreement with the human expert. In situations where there is wide agreement among experts, or for welldefined protocol studies, such a system could be very useful in narrowing the choices for a final plan. Even when wide agreement does not exist, the system can provide the advantage of consulting with several different experts and also serve as a valuable teaching tool. A disadvantage is that the method does not incorporate a strategy or criteria for optimization. Kalet and Jacky (81) describe evaluation of a rule-based expert system incorporating clinical knowledge about radiotherapy treatment techniques for head and neck cancer. The rules are used to recommend whether or not to treat, treatment techniques, doses, and possible beam configurations. The intent is to algorithmically drive planning calculations toward an acceptable result. At the time of the report the system was incomplete and lacked a method for optimizing beam set-up parameters such as placement of central ray, collimator and gantry angles, wedges, or weights. The authors conclude that rather than algorithmically optimizing a treatment plan, such a system could best be used when coupled to a treatment simulation program to create an interactive knowledge-based treatment design system. The expert system would serve as an on-line consultant to assist the physician in arriving at a final plan. Artificial intelligence methods as applied to treatment planning and many other fields are the focus of a strong research effort.

Scoring Radiation Treatment Plans The value of a given treatment plan is a measure of its tumor controlprobability (TCP) and its normul tissue complication probability (NTCP). Given the paucity of good clinical data, it is possible to build many mathematical models to calculate these quantities. Goitein and Niemierko (82) have recently critically reviewed a number of models and concluded that all suffered from trying to represent “. . . extremely complex and at best poorly understood systems with very simplistic models which use very few parameters.” Nevertheless, as Goitein has stated, the radiation dose description alone is itself a model, and in 3D does not provide an adequate score of a treatment plan. TCP Calculation Goitein (7) and Goitein and Schultheiss (83) have developed a model for calculating TCP when the dosage


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across the tumor is nonhomogeneous. The model is based on the assumption that local tumor control is uncorrelated from one region of the lesion to another and depends only on the volume of given tumor volume, V, and the dose, D, delivered to that volume. Under these assumptions the tumor control probability function, TCP (V , D) , will have a simple dependence on V as illustrated in the following example: Consider a tumor whose control probability is 50% for some dose; then the TCP for each half of the tumor must be (.50)1/2 = 71 %, since the TCP for the whole tumor is just the probability of controlling both halves (71 * 71 = .50).Thus: TCP(Vl/2, D) = TCP(V, D)’12

(1)

Thus, in general:

The variation of TCP with dose D is more difficult to determine, depending as it must, on rather sparse clinical data. Goitein (7) suggests that the dose response curve between the 20% and 80% TCP depends linearly on the log of the dose. Thus if the dose is known that will result in a TCP of 50% for a given tumor (D50), the TCP for any other dose can be approximated by TCP(V, D) = TCP(V, D50)

+ 40% *log (D/D50),

as long as 20% 5 TCP(V, D)

homogeneous one. Most methods start by constructing a dose-volume histogram (DVH) for the irradiated organ, that is, a plot of radiation dose vs partial volume of the organ irradiated to that dose (see Austin-Seymour et al. (84) for a DVH analysis of liver radiation tolerance). The problem is to transform the DVH into a NTCP for that organ; that is, to assign a single number to a given DVH for ease of comparison. According to Kutcher and Burman ( 8 5 ) , each volume of a normal tissue, irradiated to dose Di can be replaced by a small volume Veff irradiated to the dose D,,,, the maximum dose given to any part of the organ. Veff is calculated from Eq. (4):

(3)

(4) With the repeated use of Eq. (4) an inhomogeneously irradiated organ can be replaced with an organ of an equivalent NTC P that has under gone homogeneous partial irradiation. Once the histogram has been transformed, one only needs to know the NTCP of that organ that has had a partial irradiation with dose D, to volume VeR.Efforts are being made to determine this information (86). Of course other analyses are possible (82). Furthermore, simply calculating the TCP, and NTCP’s for the various normal tissues at risk is not sufficient; these scores, in turn, must be somehow blended into a final numerical score. Very little is known at present, about how to do this.

I80%.

Treatment Reproducibility NTCP Calculation Unlike TCP, normal tissue complication probabilities (NTCP) cannot be determined from the product of the dose-adjustedpartial complication probabilities, as the injury need not extend to the entire organ to produce a complication. For example, damage to even a small section of spinal cord can lead to paralysis, hardly less devastating than damage to a large volume of the cord. Thus the tolerance dose for spinal cord probably depends only weakly on the volume treated. On the other hand, for some organs (such as lung) the tolerance dose may be very high for small volumes and decrease sharply as the treated volume increases. One of the major difficulties in calculating an NTCP from a given treatment plan is that, unlike the usual case with tumor, the dose distribution across normal tissue is frequently very inhomogeneous. This inhomogeneous dose distribution must thus be related to an equivalent

As previously discussed, radiation portal films remain the single most important quality control measure of treatment delivery currently available. However, methods to improve the quality of these films have met with only modest success. Recently, it has been shown that digital enhancement techniques hold out great promise of making portal films more readable (87). Perhaps the simplest and best known method of digital enhancement is that used to make CT scans more readable, intensity widowing. The following example illustrates how intensity windowing works. Suppose a film has been digitized into pixels with 1024 (21° or 10 bits) grey levels. That is, white is assigned level 1024, black level 1, and all grey levels something in between. If the area of interest has grey levels between, say, 400 and 600 the unaided human eye will see little contrast. If intensity windowing is applied, all grey levels of 600and above would be remapped to white, and all grey levels 400 or less

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Recent Advances in Radiotherapy Treatment Planning would be remapped to black. Then the pixels in the range from 400 to 600 can be stretched out” in a linear fashion over the entire dynamic range of the new film. Although intensity windowing has been used successfully with CT images, it is not useful for digitized port films because the lost information above and below the windowing threshold proves to be important to a proper interpretation of the film. A more sophisticated strategy for displaying recorded grey levels is that of histogram equalization (HE). In this technique each pixel is remapped to a grey level proportional to its rank with respect to all the other pixels. Thus in the ongoing example all the pixels in the top 1/1024 of the list would be remapped as 1024 (pure white). Pixels in the next 1/1024 would be remapped to 1023 and so on. Finally the darkest pixels (the bottom 1/1024) would be remapped to 1 (pure black). This method avoids wasting any display space and at the same time does not delete information as in intensity windowing. However, the images are still of low contrast because of the compression of the display range. Adaptive histogram equalization (AHE) is a variant of HE independently developed by Pizer (88), and others, that can greatly enhance any digitized image. AHE also uses mapping by rank but this time rank is based on a local area surrounding each pixel rather than on the entire image. This local area, typically 1/64 of the image area, centered at each pixel is known as the contextual region. AHE, then, maximizes local contrast by mapping small local contrast differences into the entire display range. AHE can, in fact, result in overenhancement. Consider a small area of homogeneous grey scale (very low contrast) where pixels have a grey level range from 400-405. Under AHE the pixel at 405 would remap to 1024, the pixel at 400 would remap to l ! The problem of overenhancement has been addressed by the use of conrrastlimited AHE (CLAHE) (89). In this variant of AHE, the amount of possible enhancement is “clipped” at a certain level. A clip level of 10, for example, means that the difference between the display levels of two pixels in the enhaced image can be no more than 10 times the difference between their grey levels in the original image. In the last example, then the remapped grey levels of the pixel at 400 and at 405 would be 375 and 425 [a separation of 50, or 10 * (405-400)]. CLAHE has been demonstrated to be of value for enhancing portal films (87) but additional work remains to be done. In particular, CLAHE tends to degrade sharply contrasted edges which are often useful in reading portal films. Methods whereby the contextual region size and/or clip level are automatically


“

adapted to each area of the film hold promise in solving this problem (90). There has been great interest recently in direct digital capture and real-time display or radiation portal films (91-93). Ultimately it should be possible to automatically port and enhance all radiation treatment fields.

Advances in Radiation Dose Calculations Photon dose calculation algorithms have been reviewed recently by Cunningham (94,95). Most of the limitations of two-dimensional algorithms pointed out by Goitein in 1982 (34) remain largely unresolved. Calculation inaccuracies remain in the buildup tissue interface and penumbra regions, at large depths, under collimators and blocks, and in narrow sections of shaped fields. Also, twodimensional algorithms typically do not consider important information, such as inhomogeneities and patient shape outside the plane of calculation. Any threedimensional algorithms are only extensions of twodimensional methods and suffer from the same limitations. Additional problems with three-dimensional dose calculations are the speed of calculation and the lack of suitable methods for presentation of the final results, as previously discussed. Traditional methods for calculating dose distributions rely on the use of macroscopic beam measurements. Since data cannot be measured for all possible treatment conditions, a standard data set is used for all situations. Usually the macroscopic measurements are manipulated in various ways, such as separation into primary and scatter components and extrapolation to small field sizes, so as to facilitate use of an empirical or semiempirical calculation method. Unfortunately, the use of macroscopic measurements and empirical calculation methods obscures the basic physics and microscopic nature of dose deposition. Thus all these methods are prone to uncertainties Recent work in the development of dose calculation algorithms has focused on placing them on a more fundamental basis (96-99). The most fundamental way to calculate a dose distribution from first principles is to use the Monte Carlo technique (100). Although a full Monte Carlo technique is at present computationally impractical, it and other methods (96,101), can be used to calculate the dose distribution around a point-the point spread function-due to many photon interactions at that point. In contrast with macroscopic measurements, the point spread function encapsulates the basic physics of dose deposition, such as electron transport and multiple photon scattering. Compared

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with a straight Monte Carlo approach it is more practical to use the point spread function with a convolution technique to calculate true three-dimensional dose distributions (100). Practical ways to solve the convolution integral in the presence of inhomogeneities are also underway (102,103). Three-dimensional dose calculations need to be rapid enough so that it is practical to generate at least a few alternative plans. However, since the parameters that can be varied to optimize a plan are many, and the values those parameters can have are large in number, it is important to be able to compute isodose distributions as rapidly as possible to adequately sample the search space of possible treatment arrangements. In recent years, work to improve the speed of calculation has been secondary to developing better algorithms. Some progress has been made however. Niemierko and Goitein (104) reported that, compared with uniform grid spacing, variable spacing using fewer grid points resulted in more than an order of magnitude increase in the speed of calculating a threedimensional dose distribution with essentially no loss in accuracy. Also modern computers with parallel architecture and the ability to execute vectorized code promise improvements in speed of perhaps several orders of magnitude compared with computers now found in most radiotherapy departments.

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16. Address reprint requests to: Julian Rosenman, Ph .D., M.D., Department of Radiation Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.

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REFERENCES I . Kramer S: Tumor extent as a determining factor in radiotherapy of glioblastomas. Acta Radio1 8:111-117, 1959. 2. Salazar OM, Rubin P: The spread of glioblastoma multiforme as a determining factor in the radiation treated volume. Int J Radiat Oncol Biol Phys 1:627-637, 1976. 3. Tepper JE, Padiial, TN: The role of computed tomography in treament planning. In Radiation Therapy Planning. Edited by NM Bleehen, E Glatstein, JL Haybittle. Marcel Dekker Inc., New York, 1983, pp 139-158. 4. Suit HD, Westgate SJ: Impact of improved local control on survival. Int J Radiat Oncol Biol Phys 12:453-458, 1986. 5 . Coleman CN: Hypoxic radiosensithers: Expectations and progress in drug development. Int J Radiat Oncol Biol Phys 11:323-329, 1985. 6. Suit HD, Tepper JE: Impact of improved local control on survival in patients with soft tissue sarcoma. Int J Radiat Oncol Biol Phys 12:699-700, 1986. 7. Goitein M: The utility of computed tomography in radiation

19.

20.

21.

22.

23.

24. 25.

therapy: An estimate of outcome. Int J Radiat Oncol Biol Phys 5:1799-1807, 1979. Perez CA, Bauer M, Edelstein S et al: Impact of tumor control on survival in carcinoma of the lung treated with irradiation. Int J Radiat Oncol Biol Phys 12:539-547, 1986. Million RR, Cassisi NJ: Major salivary gland tumors. In Management of Head and Neck Cancer A Multidisciplinary Approach. Edited by RR Million, NJ Cassisi. J.B. Lippincott Co., Philadelphia, 1984, p 541. Saunders W, Chin LM: Innovative techniques: Dynamic therapy and utilization of noncoplanar beams. In Syllabus: A Categorical Course in Radiution Therapy Treatment Planning. Edited by BR Paliwal, ML Griem. RSNA, Division of Editorial and Publishing Services, Oak Brook, IL, 1986, pp 123-128. Lichter AS: Clinical practice of modern radiation therapy treatment planning. In Sylbbus: A Categorical Course in Radiation Therapy Treatment Planning. Edited by BR Paliwal, ML Griem. RSNA, Division of Editorial and Publishing Services, Oak Brook, IL, 1986, pp 7-12. Bauer-Kirpes B, Schlegel W, Boesecke R et al: Display or organs and isodoses as shaded 3-D objects of 3-D therapy planning. Int J Radiat Oncol Biol Phys 13:135-140, 1987. Marks JE, Bedwinek JM, Lee F et al: Dose-response analysis for nasopharyngeal carcinoma. Cancer 50: 1042-1050, 1982. Doss LL: Localization error and local recurrence in upper airway carcinoma. In Proceedings of the Workshop on Quality Control in a Radiotherapy Department. Edited by LE Reinstein. CALGB, New York, 1979. Perez CA, Stanley K, Grundy G et al: Impact of radiation technique and tumor extent in tumor control and survival of patients with unresectable non-oat cell carcinoma of the lung: Report by the RTOG. Cancer 50: 1091-1099, 1982. White JE, Chen T, McCracken J et al: The influence of radiation therapy quality control on survival, response and sites of relapse in oat cell carcinoma of the lung. Cancer 50:1084-1090, 1982. Marks JE, Haus AG, Sutton HG et al: Localization error in the radiotherapy of Hodgkin’s disease and malignant lymphoma with extended mantle fields. Cancer 43: 83-90, 1974. Kinzie JJ, Hanks GE, Maclean CJ et al: Patterns of care study: Hodgkin’s disease, relapse rates and adequacy of portals. Cancer 5212223-2226, 1983. Boyer AL: Patient positioning and immobilization devices. In Radiation Oncology Physics-1986. AAPM Monograph No. 15. Edited by JG Kereiakes, HR Elson, CG Born. American Institute of Physics, New York, 1987, pp 438-446. Sherouse GW, Bourland JD, Reynolds K et al: Virtual simulation in the clinical setting: Some practical considerations. Int J Radiat Oncol Biol Phys (in press). Byhardt RW, Cox JD, Homburg A et al: Weekly localization films and detection of field placement error. Int J Radiat Oncol Biol Phys 4:881-887, 1978. Marks JE, Haus AG, Sutton HG et al: The value of frequent treatment verification films in reducing localization error in the irradiation of complex files. Cancer 37:2755-2761, 1976. Amok HI, Reinstein LE, Lagueux B: A quantitative assessment of portal film contrast as a function of beam energy. Med Phys 13~711-716,1986. Reinstein LE, Durham M, Tefi M et al: Portal film quality: A multiple institutional study. Med Phys 11:555-557, 1984. Droege RT, Bjarngard BE: Influence of metal screens on contrast in megavoltage x-ray imaging. Med Phys 6:487-492, 1979.

479


Recent Advances in Radiotherapy Treatment Planning 26. Galkin BM, Wu RK, Suntharlingam N: Improved technique for obtaining teletherapy portal radiographs with high-energy photons. Radiology 127:828-830, 1978. 27. Wolf JN, Kalisher L, Considine B: Cobalt-60 treatment field verification by xeroradiography. Am J Roentgen01 118:916-918, 1973. 28. Fingerhut AG, Fountinelle PM: Xeroradiography in a radiation therapy department. Cancer 34:78-82, 1974. 29. Reinstein LE, Orton CG: Contrast enhancement of high energy radiotherapy films. Br J Rad 52:880-887, 1979. 30. Biggs RJ,Goitein M, Russell MD: A diagnostic x-ray field verification device for 10 MV linear accelerator. Int J Radiat Oncol Biol Phys 11:635-643, 1985. 31. Cox JD, Azarnia N, Byhardt RW et al: Hyperfractionated radiation therapy (1.2 Gy b.i.d.) with 69.6 Gy total dose increases survival in favorable patients with stage I11 non-small cell carcinoma of the lung. Report of RTOG 83-1 1. Proc ASCO, 8:220, 1989. 32. Mah K, Van Dyk J, Keane T et al: Acute radiation-induced pulmonary damage: A clinical study on the response to fractionated radiation therapy. Int J Radiat Oncol Biol Phys 13:179-188, 1987. 33. Tepper JE, Cohen AM, Wood WC et al: Postoperative radiation therapy of rectal cancer. Int J Radiat Oncol Biol Phys 13:5-10, 1987. 34. Goitein M: Limitations of two-dimensional treatment planning programs. Med Phys 4580-586, 1982. 35. Ling CC, Rogers CC, Morton RJ (Eds): Computed Tomography in Radiation Therapy. Raven Press, New York, 1983. 36. Goitein M, Suit HD, Gragoudas et al: Potential for low LET charged particle radiation therapy in cancer. Rad Res 104:S297-309, 1985. 37. Chen GTY, Verhy LJ: Charged particle dosimetry. In Radiation Oncology Physics-1 986, AAPM Monograph No. 15. Edited by JG Kereiakes, HR Elson, CG Born. American Institute of Physics, New York, 1987, pp 393-426. 38. Austin-Seymour M, Munzenrider JE, Goitein M et al: Progress in low LET heavy particle therapy: Intracranial and paracranial tumors and uveal melanomas. Rad Res 104:S219-226, 1985. 39. Reinstein LE, McShan D, Webber BM, Glicksman AS: A computer assisted three-dimensional treatment planning system. Radiology 127:259-264, 1978. 40. Glatstein E, Lichter AS, Fraass BA et al: The imaging revolution and radiation oncology: Use of CT, ultrasound, and NMR for localization, treatment planning and treatment delivery. Int J Radiat Oncol Biol Phys 11:299-314, 1985. 41. Goitein M, Abrams M: Multi-dimensional treatment planning: I. Delineation of anatomy. Int J Radiat Oncol Biol Phys 9:777-787, 1983. 42. Goitein M, Abrams M, Rowel1 D et al: Multi-dimensional treatment planning: 11. Beam’s eye-view , back projection, and projection through CT sections. Int J Radiat Oncol Biol Phys 9:789-797, 1983. 43. Dahlin H, Lamm IL, Landberg T et al: User requirements on CTbased computed dose planning systems in radiation therapy: Presentation of “check lists.” Comp Programs Biomed 16:131-138, 1983. 44. Houlard JP, Dutreix, A: 3D display of radiotherapy treatment plans. In Proceedings of the 8th International Conference on the Use of Computers in Radiotherapy. Elsevier Science Publishers, North Holland, 1984, p 219. 45. Galvin JM, Turrisi AT, Cheng E: Treatment simulation using a CT unit. In Radiation Oncology Physics-1986, AAPMMonograph

46.

47.

48. 49.

50.

51.

52.

53.

54.

55.

56.

57.

58.

59.

60.

61.

62.

No. 15. Edited by JG Kereiakes, HR Elson, CG Born. American Institute of Physics, New York, 1987, pp 462-478. Nagata Y, Nishidai T, Abe M et al: CT simulator: A new treatment planning system for radiotherapy. Int J Radiat Oncol Biol Phys 13(suppl): 176, 1987 (abstr). Moohan R, Barest G, Brewster L et al: A comprehensive threedimensional radiation treatment planning system. Int J Radiat Oncol Biol Phys 15:481-495, 1987. Kalet U,Jacky JP: A research-oriented treatment planning program system. Comput Prog Biomed 14:85-98, 1982. Rosenman J, Sherouse GW, Chaney EL et al: Virtual simulation: Initial clinical results. Int J Radiat Oncol Biol Phys (in press). Mills PH, Fuchs H, Pizer SM, Rosenman JG: IMEX: A tool for image display and contour rnangement in a windowing environment. Proc SPIE 1091:R132-142, 1989. Unser M, Eden M: Multiresolution feature extraction and solution for texture segmentation. IEEE Transact Pattern Anal Machine Intell 11:717-727, 1989. Banks G, Vries JK, McLinden S: Radiologic automated diagnosis (RAD). In Proceedings of rhe Tenth Annual Symposium on Computer Applicarions in Medical Care. Edited by MF Orthner. IEEE Computer Society Press, New York, 1986, pp 228-239. Pizer SM, Gauch J, Lifshitz LM: Interactive 2D and 3D object definition in medical images based on multiresolution image description. SPIE 914:438-444, 1988. Coggins JM: Computer-aided object definition from digital images. In Future Directions of Computer-Aided Radiotherapy. AAPM Monograph San Antonio, Texas, August 13, 1988. Sponsored by: Radiation Research Program, Division of Cancer Institute, National Cancer Institute, Bethesda, MD. Sherouse GW, Novins K, Chaney EL: Computation of digitally reconstructed radiographs for use in radiotherapy treatment design. Int J Radiat Oncol Biol Phys 17 (in press). Sherouse GW, Mosher CE, Novins K et al: Virtual simulation: Concept and implementation. In Proceedings of the 9th International Conference on the Use of Computers in Radiation lkerapy. Edited by AD Bruinvis, PH van der Giessen, HJ van Kleffens, FW Wittkhper. Elsevier Science Publishers B.V., Amsterdam, 1987, pp 433-436. Kessler ML, Pitluck S, Chen GTY: Techniques and applications of image correlation in radiotherapy treatment planning. Front Radiat Ther Oncol 21:25-32, 1987. Kessler ML: Computer techniques for correlating NMR and Xray Ct imaging for radiotherapy treatment planning. In Proceedings of the 9th International Conference on the Use of Computers in Radiation Therapy. Edited by AD Bruinvis, PH van der Giessen, HJ van Kleffens, Wittkamper. Elsevier Science Publishers B.V., Amsterdam, 1987, pp 441-444. Chen GTY, Kessler ML, Pitluck S: Structure transfer in threedimensional medical imaging studies. Proc NCGA, 171-177, 1985. Pelizzari CA, Chen GTY, Registration of multiple diagnostic imaging scans using surface fitting. In Proceedings of rhe 9th International Conference on the Use of Computers in Radiation lherapy. Edited by AD Bruinvis, PH vand der Giessen, HJ van Kleffens, FW Wittkamper. Elsevier Science Publishers B.V., Amsterdam, 1987, pp 437-440. Chin LM, Kijewski PK, Svensson GK et al: Dose optimization with computer controlled gantry rotation, collimator motion, and dose rate variation. Int J Rad Oncol Biol Phys 9:723-729, 1983. Smith AR: Radiotherapy in the year 2000: Dynamic conformal

Rosenman et al.

480

63.

64.

65.


66.

67.

68.

69. 70. 71.

72.

73.

74.

75.

76.

77.

78.

79.

80.

therapy. In Proceedings of the Eleventh Variant Users Meeting (Marco Island, Florida, May 11-13, 1986) published by Varian, pp 31-35. Mohan R, Brewster LJ, Barest G et al: Arbitrary oblique image sections for 3-D radiation treatment planning. Int J Radiat Oncol Biol Phys 13:1247-1254, 1987. Phong BT: Illumination for computer generated images. Ph.D. Thesis, University of Utah, 1973. Fuchs H, Goldfeather, Hultquist JP et al: Fast spheres, shadows, textures, transparencies,and image enhancements in pixel-planes. Comput Graph Proc 1985 SIGGRAPH Conf 19:11 1-120, 1983. Rosenman J, Sherouse GW, Fuchs H et al: Three-dimensional display techniques in radiation therapy treatment planning. Int J Radiat Oncol Biol Phys. 16:263-269, 1989. Rosenman J: 3D Imaging in radiotherapy treatment planning. In 3 - 0 Imaging in Medicine. Edited by J Udupa, G Herman. Lewis Pub, Chelsea, MI (in press). Chaney E, Rosenman J, Sherouse, GW et al: Three-dimensional displays of brain and prostate implants. EndolHyperthennia Oncology 2:93-99, 1986. Levoy M: Display of surfaces from volume data. IEEE Comput Graph Appl 8:29-37, 1988. Drebin RA, Cahenter L, Hanrahan P: Volume rendering. Comput Graph 22:65-74, 1988. Fuchs H, Poulton J, Eyles J et al: Pixel-Planes 5: A heterogeneous multiprocessor graphics system using processor-enhanced memories. Comput Graph Proc SIGGRAPH ‘8923:79-88, 1989. Hope CS, Cain 0: A computer program for optimized stationary beam treatment planning using score functions. Comp Programs Biomed 2:221, 1972. Schultheiss TE, El-Mahdi AM: Statistical decision thary applied to radiation therapy treatment decisions. Proceedings: Sixth Annual Symposium on Computer Applications in Medical Care. Edited by BI Blum. Computer Society Press, New York, 1982, pp 978-982. Schultheiss TE: Treatment planning by computer decision. In Proceedings of the 8th lntemtional Conference on the Use of Computers in Radiotherapy. Elsevier Science Publishers, North Holland, 1984, pp 225-229. Walbartst AB, Chin LM, Svensson GK: Optimization of radiation therapy: Integral-response of a model biological system. Int J Radiat Oncol Biol Phys 8:1761-1769, 1982. Gallagher TL: Optimization of external radiation beams for therapy planning. Ph.D. Thesis, Washington University, St. Louis, MO, 1976. Redpath AT, Vickery BL, Duncan W: A new technique for radiotherapy planning using quadratic programming. Phys Med Biol 21:781, 1976. Brahme A: O n the optimal dose distribution for eradication of heterogeneous tumors. In Proceedings of the 9th International Conference on the Use of Computers in Radiation Iherapy. edited by AD Bruinvis, PH van der Giessen, HJ van Kleffens, FW Wit&&per. Elsevier Science Publishers B.V., Amsterdam, 1987, pp 119-122. Censor Y, Altschuler MD Powlis WD: A computational solution of the inverse problem in radiation-therapy treatment planning. Appl Math Comp 25:57-87, 1988. Altschuler MD, Whittington R: Interactive statistical packages to assist in radiotherapy management of prostate cancer. In Proceedings of the 9th International Conference on the Use of Computers in Radiution Therapy. Edited by AD Bruinvis, PH van

81.

82.

83.

84.

85.

86. 87.

88.

89.

90.

91. 92. 93.

94.

95.

96. 97.

der Giessen, HJ van Kleffens, FW Wi-per. Elsevier Science Publishers B.V., Amsterdam, 1987, pp 549-552. Kalet IJ, Jacky JP: Knowledge-based computer simulation for radiation therapy planning. In Proceedings of the 9th International Conference on the Use of Computers in Radiation Therapy. Edited by AD Bruinvis, PH van der Giessen, HJ van Kleffens, F W Wittkamper. Elsevier Science Publishers B. V., 1987, pp 553-556. Goitein M. Niemierko A: Biologically based models for scoring treatment plans. In Future Directions of Computer-Aided Radiotherapy, AAPM Monograph San Antonio, Texas, August 13, 1988. Sponsored by: Radiation Research Program, Division of Cancer Institute, National Cancer Institute, Bethesda, MD. Goitein M, Schultheiss TE: Strategies for treating possible tumor extension: Some theoretical considerations. Int J Radiat Oncol Biol Phys 11:1519-1528, 1985. Austin-Seymour MM, Chen GTY, Castro JR et al: Dose volume histogram analysis of liver radiation tolerance. Int J Radiat Oncol Biol Phys 12:31-35, 1986. Kutcher GJ, Burman C: Calculation of complication probability factors for non-uniform normal tissue irradiation: The effective volume method. Int J Radiat Oncol Biol Phys 16:1623-1630, 1989. Lyman JT: Complication probability as assessed from dosevolume histograms. Rad Res 104:S13-S19, 1985. Sherouse GW, Rosenman J, McMurry HL et al: Automatic digital contrast enhancement of radiotherapy films. Int J Radiat Oncol Biol Phys 13:801-806, 1987. Pizer SM, Austin ID,Perry JR et al: Adaptive histogram equalization for automatic contrast enhancement of medical images. Proc SPIE 626:242-250, 1986. Pizer SM, Amburn EP, Austin JD et al: Adaptive histogram equalization and its variations. Computer Vision, Graphics and Image Proc 39:355-368, 1987. Cromartie R, Pizer SM, Rosenman JG: Contrast control reflecting local intensitiesand edges. Visualizationin Biomedical Computing Conference, 22-25 May 1990 (submitted). Leong J: A digital imaging processing system for high energy x-ray portal images. Phys Med Biol 29:1527-1535, 1984. Meertens H: Digital processing of high-energy photon beam images. Med Phys 12:lll-113, 1985. Herk HV, Meertens H: A digital imaging system for portal verification. In Proceedings of the 9th International Conference on the Use of Computers in Radiation Therapy. Edited by AD Bruinvis, PH van der Giessen, HJ van Kleffens, FW W i h p e r . Elsevier Science Publishers B.V., Amsterdam, 1987, pp 371-373. Cunningham, JR: Computer algorithms for photon beams. In Radim’on OncologyPhysics--1986, AAPM Monogmph 15. Edited by JG Kereiakes, HR Elson, CG Born. American Institute of Physics, New York, 1987, pp 512-521. Cunningham,JR: Calculation and optimization for dose planning in radiation therapy. In Syllabus: A Categorical Course in Radiation Therapy Treatment Planning. Edited by BR Paliwal, ML Griem. RSNA, Oak Brook IL, 1986, pp 23-32. Boyer A, Mok E: A photon distribution model employing convolution calculations. Med Phys 12:169-177, 1985. Mackie TR, Scrimger JW, and Battista JJ: A convolution method of calculating dose for 15MV x rays. Med Phys 12:188-196, 1985.



98. Mohan R, Chui C, Lidofsky L: Differential pencil beam computation model for photons. Med Phys 13:64-73, 1982. 99. Ahnesjo A, Andreo P, and Brahme A: Calculation and application of point spread functions for treatment planning and high energy photon beams. Acta Oncol 26:49, 1987. 100. Dean RD: A scattering kernel for use in true three-dimensional dose calculations. Med Phys 7:429, 1980. 101. Chui CH, and Mohan R: Extraction of pencil beam kernels by the deconvolution method. Med Phys 15:138-144, 1988.

48 1 102. Boyer AL, Mok EC: Calculation of photon dose distributions in an inhomogeneous medium using convolutions. Med Phys 13:503-509, 1986. 103. Ahnesjo A: Collapsed cone convolution of radiant energy for photon dose calculation in heterogeneous media. Med Phys 16~577-592, 1989. 104. Niemierko A, Goetein M: The use of variable grid spacing to accelerate dose calculations. Med Phys 16:357-365, 1989.

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