Automatic selection of non-coplanar beam directions for three-dimensional conformal radiotherapy

Automatic planning for functional lung avoidance radiotherapy based on function-guided beam angle selection and plan optimization

Objective.This study aims to develop a fully automatic planning framework for functional lung avoidance radiotherapy (AP-FLART).Approach.The AP-FLART integrates a dosimetric score-based beam angle selection method and a meta-optimization-based plan optimization method, both of which incorporate lung function information to guide dose redirection from high functional lung (HFL) to low functional lung (LFL). It is applicable to both contour-based FLART (cFLART) and voxel-based FLART (vFLART) optimization options. A cohort of 18 lung cancer patient cases underwent planning-CT and SPECT perfusion scans were collected. AP-FLART was applied to generate conventional RT (ConvRT), cFLART, and vFLART plans for all cases. We compared automatic against manual ConvRT plans as well as automatic ConvRT against FLART plans, to evaluate the effectiveness of AP-FLART. Ablation studies were performed to evaluate the contribution of function-guided beam angle selection and plan optimization to dose redirection.Main results.Automatic ConvRT plans generated by AP-FLART exhibited similar quality compared to manual counterparts. Furthermore, compared to automatic ConvRT plans, HFL mean dose,V20, andV5were significantly reduced by 1.13 Gy (p< .001), 2.01% (p< .001), and 6.66% (p< .001) respectively for cFLART plans. Besides, vFLART plans showed a decrease in lung functionally weighted mean dose by 0.64 Gy (p< .01),fV20by 0.90% (p= 0.099), andfV5by 5.07% (p< .01) respectively. Though inferior conformity was observed, all dose constraints were well satisfied. The ablation study results indicated that both function-guided beam angle selection and plan optimization significantly contributed to dose redirection.Significance.AP-FLART can effectively redirect doses from HFL to LFL without severely degrading conventional dose metrics, producing high-quality FLART plans. It has the potential to advance the research and clinical application of FLART by providing labor-free, consistent, and high-quality plans.

A data-driven approach to optimal beam/arc angle selection for liver stereotactic body radiation therapy treatment planning

BACKGROUND

Stereotactic body radiation therapy (SBRT) for liver cancer has shown promising therapeutic effects. Effective treatment relies not only on the precise delivery provided by image-guided radiation therapy (IGRT) but also high dose gradient formed around the treatment volume to spare functional liver tissue, which is highly dependent on the beam/arc angle selection. In this study, we aim to develop a decision support model to learn human planner's beam navigation approach for beam angle/arc angle selection for liver SBRT.

METHODS

A total of 27 liver SBRT/HIGRT patients (10 IMRT, 17 VMAT/DCA) were included in this study. A dosimetric budget index was defined for each beam angle/control point considering dose penetration through the patient body and liver tissue. Optimal beam angle setting (beam angles for IMRT and start/terminal angle for VMAT/DCA) was determined by minimizing the loss function defined as the sum of total dosimetric budget index and beam span penalty function. Leave-one-out validation was exercised on all 27 cases while weighting coefficients in the loss function was tuned in nested cross validation. To compare the efficacy of the model, a model plan was generated using automatically generated beam setting while retaining the original optimization constraints in the clinical plan. Model plan was normalized to the same planning target volume (PTV) V100% as the clinical plans. Dosimetric endpoints including PTV D98%, D2%, liver V20Gy and total MU were compared between two plan groups. Wilcoxon Signed-Rank test was performed with the null hypothesis being that no difference exists between two plan groups.

RESULTS

Beam setting prediction was instantaneous. Mean PTV D98% was 91.3% and 91.3% (P=0.566), while mean PTV D2% was 107.9% and 108.1% (P=0.164) for clinical plan and model plan respectively. Liver V20Gy showed no significant difference (P=0.590) with 23.3% for clinical plan and 23.4% for the model plan. Total MU is comparable (P=0.256) between the clinical plan (avg. 2,389.6) and model plan (avg. 2,319.6).

CONCLUSIONS

The evidence driven beam setting model yielded similar plan quality as hand-crafted clinical plan. It is capable of capturing human's knowledge in beam selection decision making. This model could facilitate decision making for beam angle selection while eliminating lengthy trial-and-error process of adjusting beam setting during liver SBRT treatment planning.

Beam Angle Optimization for Double-Scattering Proton Delivery Technique Using an Eclipse Application Programming Interface and Convolutional Neural Network

To automatically identify optimal beam angles for proton therapy configured with the double-scattering delivery technique, a beam angle optimization method based on a convolutional neural network (BAODS-Net) is proposed. Fifty liver plans were used for training in BAODS-Net. To generate a sequence of input data, 25 rays on the eye view of the beam were determined per angle. Each ray collects nine features, including the normalized Hounsfield unit and the position information of eight structures per 2° of gantry angle. The outputs are a set of beam angle ranking scores (S_beam) ranging from 0° to 359°, with a step size of 1°. Based on these input and output designs, BAODS-Net consists of eight convolution layers and four fully connected layers. To evaluate the plan qualities of deep-learning, equi-spaced, and clinical plans, we compared the performances of three types of loss functions and performed K-fold cross-validation (K = 5). For statistical analysis, the volumes V_27Gy and V_30Gy as well as the mean, minimum, and maximum doses were calculated for organs-at-risk by using a paired-samples t-test. As a result, smooth-L1 loss showed the best optimization performance. At the end of the training procedure, the mean squared errors between the reference and predicted S_beam were 0.031, 0.011, and 0.004 for L1, L2, and smooth-L1 loss, respectively. In terms of the plan quality, statistically, Plan_BAO has no significant difference from Plan_Clinic (P >.05). In our test, a deep-learning based beam angle optimization method for proton double-scattering treatments was developed and verified. Using Eclipse API and BAODS-Net, a plan with clinically acceptable quality was created within 5 min.

Similar-cases-based planning approaches with beam angle optimizations using water equivalent path length for lung stereotactic body radiation therapy

This study aimed to propose automated treatment planning approaches based on similar cases with beam angle optimizations using water equivalent path length (WEPL) to avoid lung and rib doses for lung stereotactic body radiation therapy (SBRT). Similar cases to an objective case were defined as cases, which were close to the objective case with respect to the Euclidean distances based on geometrical features. Initial similar-case-based (ISC) plans were generated by applying lung SBRT plans of similar cases to objective cases. Similar cases were selected using the Euclidean distances based on lung shape and geometrical features from a radiation treatment planning database with 174 cases. Beam angles of the ISC plans were optimized using a greedy algorithm based on a cost function to include absorbed doses in the lung and ribs in the WEPL. The 12 dose evaluation indices for the planning target volume, lung, spinal cord, and ribs were evaluated in the original plans, ISC plans, and optimized similar-case-based (OSC) plans with and without WEPL for 20 test cases to investigate its dosimetric impact. These findings revealed that V10 and the mean dose for the lung and V20, V30, and V40 for the ribs in the OSC plan with WEPL improved more significantly than those in the original and ISC plans. This study indicates a potential of similar cases, whose beam angle configurations were optimized with WEPL to avoid lung and rib doses in lung SBRT plans.

A fast deep learning approach for beam orientation optimization for prostate cancer treated with intensity-modulated radiation therapy

PURPOSE

Beam orientation selection, whether manual or protocol-based, is the current clinical standard in radiation therapy treatment planning, but it is tedious and can yield suboptimal results. Many algorithms have been designed to optimize beam orientation selection because of its impact on treatment plan quality, but these algorithms suffer from slow calculation of the dose influence matrices of all candidate beams. We propose a fast beam orientation selection method, based on deep learning neural networks (DNN), capable of developing a plan comparable to those developed by the state-of-the-art column generation (CG) method. Our model's novelty lies in its supervised learning structure (using CG to teach the network), DNN architecture, and ability to learn from anatomical features to predict dosimetrically suitable beam orientations without using dosimetric information from the candidate beams. This may save hours of computation.

METHODS

A supervised DNN is trained to mimic the CG algorithm, which iteratively chooses beam orientations one-by-one by calculating beam fitness values based on Karush-Kush-Tucker optimality conditions at each iteration. The DNN learns to predict these values. The dataset contains 70 prostate cancer patients - 50 training, 7 validation, and 13 test patients - to develop and test the model. Each patient's data contains 6 contours: PTV, body, bladder, rectum, and left and right femoral heads. Column generation was implemented with a GPU-based Chambolle-Pock algorithm, a first-order primal-dual proximal-class algorithm, to create 6270 plans. The DNN trained over 400 epochs, each with 2500 steps and a batch size of 1, using the Adam optimizer at a learning rate of 1 × 10-5 and a sixfold cross-validation technique.

RESULTS

The average and standard deviation of training, validation, and testing loss functions among the six folds were 0.62 ± 0.09%, 1.04 ± 0.06%, and 1.44 ± 0.11%, respectively. Using CG and supervised DNN, we generated two sets of plans for each scenario in the test set. The proposed method took at most 1.5 s to select a set of five beam orientations and 300 s to calculate the dose influence matrices for 5 beams and finally 20 s to solve the fluence map optimization (FMO). However, CG needed around 15 h to calculate the dose influence matrices of all beams and at least 400 s to solve both the beam orientation selection and FMO problems. The differences in the dose coverage of PTV between plans generated by CG and by DNN were 0.2%. The average dose differences received by organs at risk were between 1 and 6 percent: Bladder had the smallest average difference in dose received (0.956 ± 1.184%), then Rectum (2.44 ± 2.11%), Left Femoral Head (6.03 ± 5.86%), and Right Femoral Head (5.885 ± 5.515%). The dose received by Body had an average difference of 0.10 ± 0.1% between the generated treatment plans.

CONCLUSIONS

We developed a fast beam orientation selection method based on a DNN that selects beam orientations in seconds and is therefore suitable for clinical routines. In the training phase of the proposed method, the model learns the suitable beam orientations based on patients' anatomical features and omits time intensive calculations of dose influence matrices for all possible candidate beams. Solving the FMO to get the final treatment plan requires calculating dose influence matrices only for the selected beams.

Technical Note: Rotational positional error corrected intrafraction set-up margins in stereotactic radiotherapy: A spatial assessment for coplanar and noncoplanar geometry

PURPOSE

The aim of this study is to calculate setup margin based on six-dimensional (6D) corrected residual positional errors from kV cone beam computed tomography (CBCT) and from intrafraction projection kV imaging in coplanar and in noncoplanar couch positions in stereotactic radiotherapy.

METHODS

Six dimensional positional corrections were carried out before patient treatments, using a robotic couch and CBCT matching. A CBCT and stereoscopic ExacTrac image were acquired post-table position correction. Further, a series of intrafraction ExacTrac images were obtained for the variable couch position. Translational and rotational errors were identified as lateral (X), longitudinal (Y), vertical (Z); roll (Ɵ°), pitch (Φ°) and yaw (Ψ°). A total of 699 intrafraction image sets (361 coplanar and 338 noncoplanar) for 51 SRS/SRT patients were analysed. Rotational errors were corrected in terms of translational coordinates. Residual set-up margins were calculated from CBCT shifts. ExacTrac shifts give residual + intrafraction setup margins as a function of coplanar and noncoplanar couch positions.

RESULTS

The average residual positional error obtained from CBCT in X, Y, Z, Ɵ, Φ, Ψ were 0.1 ± 0.4 mm, 0.0 ± 0.6 mm, 0.0 ± 0.5 mm, 0.2 ± 0.8°, 0.1 ± 0.6° and -0.1 ± 0.7° respectively. For ExacTrac, the shits were -0.5 ± 0.9 mm, -0.0 ± 1mm, -0.6 ± 1.0mm, 0.4 ± 0.9°, -0.2 ± 0.6°, and -0.0 ± 0.8°. CBCT calculated linear setup margins in X, Y, Z direction were 0.5, 1.2, and 1 mm respectively. ExacTrac yielded coplanar and noncoplanar linear setup margins were 1.2, 1.3, 1.5, 1.4, 1.5, and 2.1 mm respectively.

CONCLUSION

CBCT-based gross residual set-up margin is equal to 1 mm. ExacTrac calculated residual plus intrafraction setup margin falls within a 2 mm range; attributed to intrafraction patient movement, table position inaccuracies, and poor image fusion in noncoplanar geometry. There could be variations in the required additional margin between centers and between machines, which require further studies.

Lung IMRT planning with automatic determination of beam angle configurations

Beam angle configuration is a major planning decision in intensity modulated radiation treatment (IMRT) that has a significant impact on dose distributions and thus quality of treatment, especially in complex planning cases such as those for lung cancer treatment. We propose a novel method to automatically determine beam configurations that incorporates noncoplanar beams. We then present a completely automated IMRT planning algorithm that combines the proposed method with a previously reported OAR DVH prediction model. Finally, we validate this completely automatic planning algorithm using a set of challenging lung IMRT cases. A beam efficiency index map is constructed to guide the selection of beam angles. This index takes into account both the dose contributions from individual beams and the combined effect of multiple beams by introducing a beam-spread term. The effect of the beam-spread term on plan quality was studied systematically and the weight of the term to balance PTV dose conformity against OAR avoidance was determined. For validation, complex lung cases with clinical IMRT plans that required the use of one or more noncoplanar beams were re-planned with the proposed automatic planning algorithm. Important dose metrics for the PTV and OARs in the automatic plans were compared with those of the clinical plans. The results are very encouraging. The PTV dose conformity and homogeneity in the automatic plans improved significantly. And all the dose metrics of the automatic plans, except the lung V_{5 Gy}, were statistically better than or comparable with those of the clinical plans. In conclusion, the automatic planning algorithm can incorporate non-coplanar beam configurations in challenging lung cases and can generate plans efficiently with quality closely approximating that of clinical plans.

A new sparse optimization scheme for simultaneous beam angle and fluence map optimization in radiotherapy planning

[Formula: see text]-minimization-based sparse optimization was employed to solve the beam angle optimization (BAO) in intensity-modulated radiation therapy (IMRT) planning. The technique approximates the exact BAO formulation with efficiently computable convex surrogates, leading to plans that are inferior to those attainable with recently proposed gradient-based greedy schemes. In this paper, we alleviate/reduce the nontrivial inconsistencies between the [Formula: see text]-based formulations and the exact BAO model by proposing a new sparse optimization framework based on the most recent developments in group variable selection. We propose the incorporation of the group-folded concave penalty (gFCP) as a substitution to the [Formula: see text]-minimization framework. The new formulation is then solved by a variation of an existing gradient method. The performance of the proposed scheme is evaluated by both plan quality and the computational efficiency using three IMRT cases: a coplanar prostate case, a coplanar head-and-neck case, and a noncoplanar liver case. Involved in the evaluation are two alternative schemes: the [Formula: see text]-minimization approach and the gradient norm method (GNM). The gFCP-based scheme outperforms both counterpart approaches. In particular, gFCP generates better plans than those obtained using the [Formula: see text]-minimization for all three cases with a comparable computation time. As compared to the GNM, the gFCP improves both the plan quality and computational efficiency. The proposed gFCP-based scheme provides a promising framework for BAO and promises to improve both planning time and plan quality.

Automatic learning-based beam angle selection for thoracic IMRT

PURPOSE

The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Intensity modulated radiation therapy (IMRT) planning is a lengthy process, which requires the planner to iterate between choosing beam angles, specifying dose-volume objectives and executing IMRT optimization. In thorax treatment planning, where there are no class solutions for beam placement, beam angle selection is performed manually, based on the planner's clinical experience. The purpose of this work is to propose and study a computationally efficient framework that utilizes machine learning to automatically select treatment beam angles. Such a framework may be helpful for reducing the overall planning workload.

METHODS

The authors introduce an automated beam selection method, based on learning the relationships between beam angles and anatomical features. Using a large set of clinically approved IMRT plans, a random forest regression algorithm is trained to map a multitude of anatomical features into an individual beam score. An optimization scheme is then built to select and adjust the beam angles, considering the learned interbeam dependencies. The validity and quality of the automatically selected beams evaluated using the manually selected beams from the corresponding clinical plans as the ground truth.

RESULTS

The analysis included 149 clinically approved thoracic IMRT plans. For a randomly selected test subset of 27 plans, IMRT plans were generated using automatically selected beams and compared to the clinical plans. The comparison of the predicted and the clinical beam angles demonstrated a good average correspondence between the two (angular distance 16.8° ± 10°, correlation 0.75 ± 0.2). The dose distributions of the semiautomatic and clinical plans were equivalent in terms of primary target volume coverage and organ at risk sparing and were superior over plans produced with fixed sets of common beam angles. The great majority of the automatic plans (93%) were approved as clinically acceptable by three radiation therapy specialists.

CONCLUSIONS

The results demonstrated the feasibility of utilizing a learning-based approach for automatic selection of beam angles in thoracic IMRT planning. The proposed method may assist in reducing the manual planning workload, while sustaining plan quality.

Similar-case-based optimization of beam arrangements in stereotactic body radiotherapy for assisting treatment planners

Objective. To develop a similar-case-based optimization method for beam arrangements in lung stereotactic body radiotherapy (SBRT) to assist treatment planners. Methods. First, cases that are similar to an objective case were automatically selected based on geometrical features related to a planning target volume (PTV) location, PTV shape, lung size, and spinal cord position. Second, initial beam arrangements were determined by registration of similar cases with the objective case using a linear registration technique. Finally, beam directions of the objective case were locally optimized based on the cost function, which takes into account the radiation absorption in normal tissues and organs at risk. The proposed method was evaluated with 10 test cases and a treatment planning database including 81 cases, by using 11 planning evaluation indices such as tumor control probability and normal tissue complication probability (NTCP). Results. The procedure for the local optimization of beam arrangements improved the quality of treatment plans with significant differences (P < 0.05) in the homogeneity index and conformity index for the PTV, V10, V20, mean dose, and NTCP for the lung. Conclusion. The proposed method could be usable as a computer-aided treatment planning tool for the determination of beam arrangements in SBRT.

Comparison of beam angle selection strategies for intracranial IMRT

PURPOSE

Various strategies to select beneficial beam ensembles for intensity-modulated radiation therapy (IMRT) have been suggested over the years. These beam angle selection (BAS) strategies are usually evaluated against reference configurations applying equispaced coplanar beams but they are not compared to one another. Here, the authors present a meta analysis of four BAS strategies that incorporates fluence optimization (FO) into BAS by combinatorial optimization (CO) and one BAS strategy that decouples FO from BAS, i.e., spherical cluster analysis (SCA). The underlying parameters of the BAS process are investigated and the dosimetric benefits of the BAS strategies are quantified.

METHODS

For three intracranial lesions in proximity to organs at risk (OARs) the authors compare treatment plans applying equispaced coplanar beam ensembles with treatment plans using five different BAS strategies, i.e., four CO techniques and SCA, to establish coplanar and noncoplanar beam ensembles. Treatment plans applying 5, 7, 9, and 11 beams are investigated. For the CO strategies the authors perform BAS runs with a 5°, 10°, 15°, and 20° angular resolution, which corresponds to a minimum of 18 coplanar and a maximum of 1400 noncoplanar candidate beams. In total 272 treatment plans with different BAS settings are generated for every patient. The quality of the treatment plans is compared based on the protection of OARs yet integral dose, target homogeneity, and target conformity are also considered.

RESULTS

It is possible to reduce the average mean and maximum doses in OARs by more than 4 Gy (1 Gy) with optimized noncoplanar (coplanar) beam ensembles found with BAS by CO or SCA. For BAS including FO by CO, the individual algorithm used and the angular resolution in the space of candidate beams does not have a crucial impact on the quality of the resulting treatment plans. All CO algorithms yield similar target conformity and slightly improved target homogeneity in comparison to equispaced coplanar setups. Furthermore, optimized coplanar (noncoplanar) beam ensembles enabled more than a 6% (5%) reduction of the integral dose. For SCA, however, integral dose was increased and target conformity was decreased in comparison to equispaced coplanar setups-especially for a small number of beams.

CONCLUSION

Both BAS strategies incorporating FO by CO and independent BAS strategies excluding FO provide dose savings in OARs for optimized coplanar and especially noncoplanar beam ensembles; they should not be neglected in the clinic.

Computer-aided beam arrangement based on similar cases in radiation treatment-planning databases for stereotactic lung radiation therapy

The purpose of this study was to develop a computer-aided method for determination of beam arrangements based on similar cases in a radiotherapy treatment-planning database for stereotactic lung radiation therapy. Similar-case-based beam arrangements were automatically determined based on the following two steps. First, the five most similar cases were searched, based on geometrical features related to the location, size and shape of the planning target volume, lung and spinal cord. Second, five beam arrangements of an objective case were automatically determined by registering five similar cases with the objective case, with respect to lung regions, by means of a linear registration technique. For evaluation of the beam arrangements five treatment plans were manually created by applying the beam arrangements determined in the second step to the objective case. The most usable beam arrangement was selected by sorting the five treatment plans based on eight plan evaluation indices, including the D95, mean lung dose and spinal cord maximum dose. We applied the proposed method to 10 test cases, by using an RTP database of 81 cases with lung cancer, and compared the eight plan evaluation indices between the original treatment plan and the corresponding most usable similar-case-based treatment plan. As a result, the proposed method may provide usable beam arrangements, which have no statistically significant differences from the original beam arrangements (P > 0.05) in terms of the eight plan evaluation indices. Therefore, the proposed method could be employed as an educational tool for less experienced treatment planners.

Characterizing the combinatorial beam angle selection problem

The beam angle selection (BAS) problem in intensity-modulated radiation therapy is often interpreted as a combinatorial optimization problem, i.e. finding the best combination of η beams in a discrete set of candidate beams. It is well established that the combinatorial BAS problem may be solved efficiently with metaheuristics such as simulated annealing or genetic algorithms. However, the underlying parameters of the optimization process, such as the inclusion of non-coplanar candidate beams, the angular resolution in the space of candidate beams, and the number of evaluated beam ensembles as well as the relative performance of different metaheuristics have not yet been systematically investigated. We study these open questions in a meta-analysis of four strategies for combinatorial optimization in order to provide a reference for future research related to the BAS problem in intensity-modulated radiation therapy treatment planning. We introduce a high-performance inverse planning engine for BAS. It performs a full fluence optimization for ≈3600 treatment plans per hour while handling up to 50 GB of dose influence data (≈1400 candidate beams). For three head and neck patients, we compare the relative performance of a genetic, a cross-entropy, a simulated annealing and a naive iterative algorithm. The selection of ensembles with 5, 7, 9 and 11 beams considering either only coplanar or all feasible candidate beams is studied for an angular resolution of 5°, 10°, 15° and 20° in the space of candidate beams. The impact of different convergence criteria is investigated in comparison to a fixed termination after the evaluation of 10 000 beam ensembles. In total, our simulations comprise a full fluence optimization for about 3000 000 treatment plans. All four combinatorial BAS strategies yield significant improvements of the objective function value and of the corresponding dose distributions compared to standard beam configurations with equi-spaced coplanar beams. The genetic and the cross-entropy algorithms showed faster convergence in the very beginning of the optimization but the simulated annealing algorithm eventually arrived at almost the same objective function values. These three strategies typically yield clinically equivalent treatment plans. The iterative algorithm showed the worst convergence properties. The choice of the termination criterion had a stronger influence on the performance of the simulated annealing algorithm than on the performance of the genetic and the cross-entropy algorithms. We advocate to terminate the optimization process after the evaluation of 1000 beam combinations without objective function decrease. For our simulations, this resulted in an average deviation of the objective function from the reference value after 10 000 evaluated beam ensembles of 0.5% for all metaheuristics. On average, there was only a minor improvement when increasing the angular resolution in the space of candidate beam angles from 20° to 5°. However, we observed significant improvements when considering non-coplanar candidate beams for challenging head and neck cases.

A NOVEL TWO PHASE RETRIEVAL MECHANISM FOR A CLINICAL CASE BASED REASONING SYSTEM FOR RADIOTHERAPY TREATMENT PLANNING

This paper presents a decision support system for radiotherapy treatment planning for head, neck and brain cancer. The aim of a treatment plan is to apply radiation to kill tumor cells, while minimizing the damage to healthy tissue and critical organs. Since treatment planning is a complex decision making process that relies heavily on the subjective experience of clinicians, we propose the use of case-based reasoning (CBR), in which problems are solved based on the solutions of similar past problems. This paper focuses on the case retrieval process of a CBR system. The attributes, which describe the cases, are selected by assessing their effect on the performance of the CBR system. We have developed a context sensitive local weighting scheme that assigns weights to attributes based on their value and the values of other attributes in the target case. A novel two phase retrieval mechanism is developed, in which each phase is optimized to retrieve a particular part of the solution. We also present an original use of fuzzy logic in order to represent nonlinearity in the similarity measure. Experiments, which evaluate the similarity measure using real brain cancer patient cases, show promising results.

Spherical cluster analysis for beam angle optimization in intensity-modulated radiation therapy treatment planning

An intuitive heuristic to establish beam configurations for intensity-modulated radiation therapy is introduced as an extension of beam ensemble selection strategies applying scalar scoring functions. It is validated by treatment plan comparisons for three intra-cranial, pancreas, and prostate cases each. Based on a patient specific matrix listing the radiological quality of candidate beam directions individually for every target voxel, a set of locally ideal beam angles is generated. The spherical distribution of locally ideal beam angles is characteristic for every treatment site and patient: ideal beam angles typically cluster around distinct orientations. We interpret the cluster centroids, which are identified with a spherical K-means algorithm, as irradiation angles of an intensity-modulated radiation therapy treatment plan. The fluence profiles are subsequently optimized during a conventional inverse planning process. The average computation time for the pre-optimization of a beam ensemble is six minutes on a state-of-the-art work station. The treatment planning study demonstrates the potential benefit of the proposed beam angle optimization strategy. For the three prostate cases under investigation, the standard treatment plans applying nine coplanar equi-spaced beams and treatment plans applying an optimized non-coplanar nine-beam ensemble yield clinically comparable dose distributions. For symmetric patient geometries, the dose distribution formed by nine equi-spaced coplanar beams cannot be improved significantly. For the three pancreas and intra-cranial cases under investigation, the optimized non-coplanar beam ensembles enable better sparing of organs at risk while guaranteeing equivalent target coverage. Beam angle optimization by spherical cluster analysis shows the biggest impact for target volumes located asymmetrically within the patient and close to organs at risk.

A 'learning-by-doing' treatment planning tutorial for medical physicists

A framework for a tutorial for treatment planning in radiation oncology physics was developed, based on the University of Washington treatment planning system Prism. The tutorial is aimed at students in Medical Physics to accompany the lectures on treatment planning to enhance their theoretical knowledge. A web-based layout was chosen to allow independent work of the students. The tutorial guides the students through three different learning modules, designed mainly to enhance their understanding of the processes involved in treatment planning but also to learn the specific features of a modern treatment planning system. Each of the modules contains four units, with the aim to introduce the relevant Prism features, practice skills in different tasks and finally check the learning outcomes with a challenge and a self-scoring quiz. A survey for students' feedback completes the tutorial. Various tools and learning methods help to create an interactive, appealing learning environment, in which the emphasis is shifted from teacher-centred to student-centred learning paradigms. In summary, Prism lends itself well for educational purposes. The tutorial covers all main aspects of treatment planning. In its current form the tutorial is self-contained but still adjustable and expandable. The tutorial can be made available upon request to the authors.

An approaching genetic algorithm for automatic beam angle selection in IMRT planning

A method named approaching genetic algorithm (AGA) is introduced to automatically select the beam angles for intensity-modulated radiotherapy (IMRT) planning. In AGA, the best individual of the current population is found at first, and the rest of the normal individuals approach the current best one according to some specially designed rules. In the course of approaching, some better individuals may be obtained. Then, the current best individual is updated to try to approach the real best one. The approaching and updating operations of AGA replace the selection, crossover and mutation operations of the genetic algorithm (GA) completely. Using the specially designed updating strategies, AGA can recover the varieties of the population to a certain extent and retain the powerful ability of evolution, compared to GA. The beam angles are selected using AGA, followed by a beam intensity map optimization using conjugate gradient (CG). A simulated case and a clinical case with nasopharynx cancer are employed to demonstrate the feasibility of AGA. For the case investigated, AGA was feasible for the beam angle optimization (BAO) problem in IMRT planning and converged faster than GA.

A comparison between 2-Step IMRT and conventional IMRT planning

BACKGROUND AND PURPOSE

2-Step intensity modulated radiation therapy (2-Step IMRT) is an IMRT segmentation procedure based on analytical approximations [Bratengeier K. 2-Step IMAT and 2-Step IMRT: a geometrical approach. Med Phys 2005;32:777-785; Bratengeier K. 2-Step IMAT and 2-Step IMRT in three dimensions. Med Phys 2005;32:3849-3861]. The aim was to benchmark it with other IMRT algorithms and to establish it as planning tool for fast IMRT application with a reduced number of segments.

MATERIALS AND METHODS

2-Step IMRT plans were compared with IMRT-solutions obtained with methods from a commercial planning system (Pinnacletrade mark TPS). The four clinical cases chosen were: paraspinal tumour, carcinoma of the prostate, head and neck carcinoma and breast carcinoma. In addition the "Quasimodo" phantom study was used to compare the quality of the 2-Step IMRT method with respect to other planning procedures in the ESTRO study.

RESULTS

The number of segments (and - to a minor degree - the monitor units per dose) of the majority of 2-Step IMRT plans was lower than for the commercial algorithms. The quality of the 2-Step IMRT-plan was comparable. In the Quasimodo comparison 2-Step IMRT plans with nine beams would place in the mid-range of all participants, whereas the 15-beam arrangements could compete with the best results.

CONCLUSIONS

2-Step IMRT is a valuable IMRT segmentation method, especially if the number of segments is to be limited (e.g. for reasons of precision, speed and leakage radiation).

Direct-aperture optimization applied to selection of beam orientations in intensity-modulated radiation therapy

Direct-aperture optimization (DAO) was applied to iterative beam-orientation selection in intensity-modulated radiation therapy (IMRT), so as to ensure a realistic segmental treatment plan at each iteration. Nested optimization engines dealt separately with gantry angles, couch angles, collimator angles, segment shapes, segment weights and wedge angles. Each optimization engine performed a random search with successively narrowing step sizes. For optimization of segment shapes, the filtered backprojection (FBP) method was first used to determine desired fluence, the fluence map was segmented, and then constrained direct-aperture optimization was used thereafter. Segment shapes were fully optimized when a beam angle was perturbed, and minimally re-optimized otherwise. The algorithm was compared with a previously reported method using FBP alone at each orientation iteration. An example case consisting of a cylindrical phantom with a hemi-annular planning target volume (PTV) showed that for three-field plans, the method performed better than when using FBP alone, but for five or more fields, neither method provided much benefit over equally spaced beams. For a prostate case, improved bladder sparing was achieved through the use of the new algorithm. A plan for partial scalp treatment showed slightly improved PTV coverage and lower irradiated volume of brain with the new method compared to FBP alone. It is concluded that, although the method is computationally intensive and not suitable for searching large unconstrained regions of beam space, it can be used effectively in conjunction with prior class solutions to provide individually optimized IMRT treatment plans.

Dynamic intensity-modulated non-coplanar arc radiotherapy (INCA) for head and neck cancer

BACKGROUND AND PURPOSE

To define the potential advantages of intensity-modulated radiotherapy (IMRT) applied using a non-coplanar dynamic arc technique for the treatment of head and neck cancer.

MATERIALS AND METHODS

External beam radiotherapy (EBRT) was planned in ten patients with head and neck cancer using coplanar IMRT and non-coplanar arc techniques, termed intensity modulated non-coplanar arc EBRT (INCA). Planning target volumes (PTV1) of first order covered the gross tumor volume and surrounding clinical target volume treated with 68-70 Gy, whereas PTV2 covered the elective lymph nodes with 54-55 Gy using a simultaneous internal boost. Treatment plan comparison between IMRT and INCA was carried out using dose-volume histogram and "equivalent uniform dose" (EUD).

RESULTS

INCA resulted in better dose coverage and homogeneity of the PTV1, PTV2, and reduced dose delivered to most of the organs at risk (OAR). For the parotid glands, a reduction of the mean dose of 2.9 (+/- 2.0) Gy was observed (p = 0.002), the mean dose to the larynx was reduced by 6.9 (+/- 2.9) Gy (p = 0.003), the oral mucosa by 2.4 (+/- 1.1) Gy (p < 0.001), and the maximal dose to the spinal cord by 3.2 (+/- 1.7) Gy (p = 0.004). The mean dose to the brain was increased by 3.0 (+/- 1.4) Gy (p = 0.002) and the mean lung dose increased by 0.2 (+/- 0.4) Gy (p = 0.87). The EUD suggested better avoidance of the OAR, except for the lung, and better coverage and dose uniformity were achieved with INCA compared to IMRT.

CONCLUSION

Dose delivery accuracy with IMRT using a non-coplanar dynamic arc beam geometry potentially improves treatment of head and neck cancer.