Accelerated iterative beam angle selection in IMRT

Dosimetric evaluation of different planning strategies for hypofractionated whole-breast irradiation technique

Purpose.The dose hotspot areas in hypofractionated whole-breast irradiation (WBI) greatly increase the risk of acute skin toxicity because of the anatomical peculiarities of the breast. In this study, we presented several novel planning strategies that integrate multiple sub-planning target volumes (sub-PTVs), field secondary placement, and RapidPlan models for right-sided hypofractionated WBI.Methods.A total of 35 cases of WBI with a dose of 42.5 Gy for PTVs using tangential intensity-modulated radiotherapy (IMRT) were selected. Both PTVs were planned for simultaneous treatment using the original manual multiple sub-PTV plan (OMMP) and the original manual single-PTV plan (OMSP). The manual field secondary placement multiple sub-PTV plan (m-FSMP) with multiple objects on the original PTV and the manual field secondary placement single-objective plan (m-FSSP) were initially planned, which were distribution-based of V105 (volume receiving 105% of the prescription dose). In addition, two RapidPlan-based plans were developed, including the RapidPlan-based multiple sub-PTVs plan (r-FSMP) and the RapidPlan-based single-PTV plan (r-FSSP). Dosimetric parameters of the plans were compared, and V105 was evaluated using multivariate analysis to determine how it was related to the volume of PTV and the interval of lateral beam angles (ILBA).Results.The lowest mean V105 (5.64 ± 6.5%) of PTV was observed in m-FSMP compared to other manual plans. Upon validation, r-FSSP demonstrated superior dosimetric quality for OAR compared to the two other manual planning methods, except for V5(the volume of ipsilateral lung receiving 5 Gy) of the ipsilateral lung. While r-FSMP showed no significant difference (p = 0.06) compared to r-FSSP, it achieved the lowest V105 value (4.3 ± 4.5%), albeit with a slight increase in the dose to some OARs. Multivariate GEE linear regression showed that V105 is significantly correlated with target volume and ILBA.Conclusions.m-FSMP and r-FSMP can substantially enhance the homogeneity index (HI) and reduce V105, thereby minimizing the risk of acute skin toxicities, even though there may be a slight dose compromise for certain OARs.

Reflections on beam configuration optimization for intensity-modulated proton therapy

Presumably, intensity-modulated proton radiotherapy (IMPT) is the most powerful form of proton radiotherapy. In the current state of the art, IMPT beam configurations (i.e. the number of beams and their directions) are, in general, chosen subjectively based on prior experience and practicality. Beam configuration optimization (BCO) for IMPT could, in theory, significantly enhance IMPT’s therapeutic potential. However, BCO is complex and highly computer resource-intensive. Some algorithms for BCO have been developed for intensity-modulated photon therapy (IMRT). They are rarely used clinically mainly because the large number of beams typically employed in IMRT renders BCO essentially unnecessary. Moreover, in the newer form of IMRT, volumetric modulated arc therapy, there are no individual static beams. BCO is of greater importance for IMPT because it typically employs a very small number of beams (2-4) and, when the number of beams is small, BCO is critical for improving plan quality. However, the unique properties and requirements of protons, particularly in IMPT, make BCO challenging. Protons are more sensitive than photons to anatomic changes, exhibit variable relative biological effectiveness along their paths, and, as recently discovered, may spare the immune system. Such factors must be considered in IMPT BCO, though doing so would make BCO more resource intensive and make it more challenging to extend BCO algorithms developed for IMRT to IMPT. A limited amount of research in IMPT BCO has been conducted; however, considerable additional work is needed for its further development to make it truly effective and computationally practical. This article aims to provide a review of existing BCO algorithms, most of which were developed for IMRT, and addresses important requirements specific to BCO for IMPT optimization that necessitate the modification of existing approaches or the development of new effective and efficient ones.

TBS-BAO: fully automated beam angle optimization for IMRT guided by a total-beam-space reference plan

Properly selected beam angles contribute to the quality of radiotherapy treatment plans. However, the beam angle optimization (BAO) problem is difficult to solve to optimality due to its non-convex discrete nature with many local minima. In this study, we propose TBS-BAO, a novel approach for solving the BAO problem, and test it for non-coplanar robotic CyberKnife radiotherapy for prostate cancer. First, an ideal Pareto-optimal reference dose distribution is automatically generated usinga priorimulti-criterial fluence map optimization (FMO) to generate a plan that includes all candidate beams (total-beam-space, TBS). Then, this ideal dose distribution is reproduced as closely as possible in a subsequent segmentation/beam angle optimization step (SEG/BAO), while limiting the number of allowed beams to a user-selectable preset value. SEG/BAO aims at a close reproduction of the ideal dose distribution. For each of 33 prostate SBRT patients, 18 treatment plans with different pre-set numbers of allowed beams were automatically generated with the proposed TBS-BAO. For each patient, the TBS-BAO plans were then compared to a plan that was automatically generated with an alternative BAO method (Erasmus-iCycle) and to a high-quality manually generated plan. TBS-BAO was able to automatically generate plans with clinically feasible numbers of beams (∼25), with a quality highly similar to corresponding 91-beam ideal reference plans. Compared to the alternative Erasmus-iCycle BAO approach, similar plan quality was obtained for 25-beam segmented plans, while computation times were reduced from 10.7 hours to 4.8/1.5 hours, depending on the applied pencil-beam resolution in TBS-BAO. 25-beam TBS-BAO plans had similar quality as manually generated plans with on average 48 beams, while delivery times reduced from 22.3 to 18.4/18.1 min. TBS reference plans could effectively steer the discrete non-convex BAO.

Graph neural networks and deep reinforcement learning for simultaneous beam orientation and trajectory optimization of Cyberknife

Objective. Despite the high-quality treatment, the long treatment time of the Cyberknife system is believed to be a drawback. The high flexibility of its robotic arm requires meticulous path-finding algorithms to deliver the prescribed dose in the shortest time.Approach. We proposed a Deep Q-learning based on Graph Neural Networks to find the subset of the beams and the order to traverse them. A complex reward function is defined to minimize the distance covered by the robotic arm while avoiding the selection of close beams. Individual beam scores are also generated based on their effect on the beam intensity and are incorporated in the reward function. Main results. The performance of the presented method is evaluated on three clinical cases suffering from lung cancer. Applying this approach leads to an average of 35% reduction in the treatment time while delivering the prescribed dose provided by the physicians.Significance. Shorter treatment times result in a better treatment experience for individual patients, reduces discomfort and the sides effects of inadvertent movements for them. Additionally, it creates the opportunity to treat a higher number of patients in a given time period at the radiation therapy centers.

Comparison of non-coplanar optimization of static beams and arc trajectories for intensity-modulated treatments of meningioma cases

Two methods for non-coplanar beam direction optimization, one for static beams and another for arc trajectories, were proposed for intracranial tumours. The results of the beam angle optimizations were compared with the beam directions used in the clinical plans. Ten meningioma cases already treated were selected for this retrospective planning study. Algorithms for non-coplanar beam angle optimization (BAO) and arc trajectory optimization (ATO) were used to generate the corresponding plans. A plan quality score, calculated by a graphical method for plan assessment and comparison, was used to guide the beam angle optimization process. For each patient, the clinical plans (CLIN), created with the static beam orientations used for treatment, and coplanar VMAT approximated plans (VMAT) were also generated. To make fair plan comparisons, all plan optimizations were performed in an automated multicriteria calculation engine and the dosimetric plan quality was assessed. BAO and ATO plans presented, on average, moderate global plan score improvements over VMAT and CLIN plans. Nevertheless, while BAO and CLIN plans assured a more efficient OARs sparing, the ATO and VMAT plans presented a higher coverage and conformity of the PTV. Globally, all plans presented high-quality dose distributions. No statistically significant quality differences were found, on average, between BAO, ATO and CLIN plans. However, automated plan solution optimizations (BAO or ATO) may improve plan generation efficiency and standardization. In some individual patients, plan quality improvements were achieved with ATO plans, demonstrating the possible benefits of this automated optimized delivery technique.

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.

Comparison of two beam angular optimization algorithms guided by automated multicriterial IMRT

PURPOSE

To compare two beam angle optimization (BAO) algorithms for coplanar and non-coplanar geometries in a multicriterial optimization framework.

METHODS

40 nasopharynx patients were selected for this retrospective planning study. IMRT optimized plans were produced by Erasmus-iCycle multicriterial optimization platform. Two different algorithms, based on a discrete and on a continuous exploration of the space search, algorithm i and B respectively, were used to address BAO. Plan quality evaluation and comparison were performed with SPIDERplan. Statistically significant differences between the plans were also assessed.

RESULTS

For plans using only coplanar incidences, the optimized beam distribution with algorithm i is more asymmetric than with algorithm B. For non-coplanar beam optimization, larger deviations from coplanarity were obtained with algorithm i than with algorithm B. Globally, both algorithms presented near equivalent plan quality scores, with algorithm B presenting a marginally better performance than algorithm i.

CONCLUSION

Almost all plans presented high quality, profiting from multicriterial and beam angular optimization. Although there were not significant differences when average results over the entire sample were considered, a case-by-case analysis revealed important differences for some patients.

Parallel beamlet dose calculation via beamlet contexts in a distributed multi-GPU framework

PURPOSE

Dose calculation is one of the most computationally intensive, yet essential tasks in the treatment planning process. With the recent interest in automatic beam orientation and arc trajectory optimization techniques, there is a great need for more efficient model-based dose calculation algorithms that can accommodate hundreds to thousands of beam candidates at once. Foundational work has shown the translation of dose calculation algorithms to graphical processing units (GPUs), lending to remarkable gains in processing efficiency. But these methods provide parallelization of dose for only a single beamlet, serializing the calculation of multiple beamlets and under-utilizing the potential of modern GPUs. In this paper, the authors propose a framework enabling parallel computation of many beamlet doses using a novel beamlet context transformation and further embed this approach in a scalable network of multi-GPU computational nodes.

METHODS

The proposed context-based transformation separates beamlet-local density and TERMA into distinct beamlet contexts that independently provide sufficient data for beamlet dose calculation. Beamlet contexts are arranged in a composite context array with dosimetric isolation, and the context array is subjected to a GPU collapsed-cone convolution superposition procedure, producing the set of beamlet-specific dose distributions in a single pass. Dose from each context is converted to a sparse representation for efficient storage and retrieval during treatment plan optimization. The context radius is a new parameter permitting flexibility between the speed and fidelity of the dose calculation process. A distributed manager-worker architecture is constructed around the context-based GPU dose calculation approach supporting an arbitrary number of worker nodes and resident GPUs. Phantom experiments were executed to verify the accuracy of the context-based approach compared to Monte Carlo and a reference CPU-CCCS implementation for single beamlets and broad beams composed by addition of beamlets. Dose for representative 4π beam sets was calculated in lung and prostate cases to compare its efficiency with that of an existing beamlet-sequential GPU-CCCS implementation. Code profiling was also performed to evaluate the scalability of the framework across many networked GPUs.

RESULTS

The dosimetric accuracy of the context-based method displays <1.35% and 2.35% average error from the existing serialized CPU-CCCS algorithm and Monte Carlo simulation for beamlet-specific PDDs in water and slab phantoms, respectively. The context-based method demonstrates substantial speedup of up to two orders of magnitude over the beamlet-sequential GPU-CCCS method in the tested configurations. The context-based framework demonstrates near linear scaling in the number of distributed compute nodes and GPUs employed, indicating that it is flexible enough to meet the performance requirements of most users by simply increasing the hardware utilization.

CONCLUSIONS

The context-based approach demonstrates a new expectation of performance for beamlet-based dose calculation methods. This approach has been successful in accelerating the dose calculation process for very large-scale treatment planning problems - such as automatic 4π IMRT beam orientation and VMAT arc trajectory selection, with hundreds of thousands of beamlets - in clinically feasible timeframes. The flexibility of this framework makes it as a strong candidate for use in a variety of other very large-scale treatment planning tasks and clinical workflows.

Recent developments in non-coplanar radiotherapy

This paper gives an overview of recent developments in non-coplanar intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). Modern linear accelerators are capable of automating motion around multiple axes, allowing efficient delivery of highly non-coplanar radiotherapy techniques. Novel techniques developed for C-arm and non-standard linac geometries, methods of optimization, and clinical applications are reviewed. The additional degrees of freedom are shown to increase the therapeutic ratio, either through dose escalation to the target or dose reduction to functionally important organs at risk, by multiple research groups. Although significant work is still needed to translate these new non-coplanar radiotherapy techniques into the clinic, clinical implementation should be prioritized. Recent developments in non-coplanar radiotherapy demonstrate that it continues to have a place in modern cancer treatment.

Beam selection for stereotactic ablative radiotherapy using Cyberknife with multileaf collimation

The Cyberknife system (Accuray Inc., Sunnyvale, CA) enables radiotherapy using stereotactic ablative body radiotherapy (SABR) with a large number of non-coplanar beam orientations. Recently, a multileaf collimator has also been available to allow flexibility in field shaping. This work aims to evaluate the quality of treatment plans obtainable with the multileaf collimator. Specifically, the aim is to find a subset of beam orientations from a predetermined set of candidate directions, such that the treatment quality is maintained but the treatment time is reduced. An evolutionary algorithm is used to successively refine a randomly selected starting set of beam orientations. By using an efficient computational framework, clinically useful solutions can be found in several hours. It is found that 15 beam orientations are able to provide treatment quality which approaches that of the candidate beam set of 110 beam orientations, but with approximately half of the estimated treatment time. Choice of an efficient subset of beam orientations offers the possibility to improve the patient experience and maximise the number of patients treated.

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.