Enhanced optimization of volumetric modulated arc therapy plans using Monte Carlo generated beamlets

Abstract

Purpose A treatment planning system (TPS) produces volumetric modulated arc therapy (VMAT) plans by applying an optimization process to an objective function, followed by an accurate calculation of the final, deliverable dose. However, during the optimization step, a rapid dose calculation algorithm is required, which reduces its accuracy and its representation of the objective function space. Monte Carlo (MC) routines, considered the gold standard in accuracy, are currently too slow for practical comprehensive VMAT optimization. Therefore, we propose a novel approach called enhanced optimization (EO), which employs the TPS VMAT plan as a starting point, and applies small perturbations to nudge the solution closer to a true objective minimum. The perturbations consist of beamlet dose matrices, calculated using MC routines on a distributed‐computing framework. Methods DICOM files for clinical VMAT plans files are exported from the TPS and used to generate input files for the EGSnrc MC toolkit. Beamlet doses are calculated using the MC routines, each corresponding to a single multileaf collimator leaf from a single control point traveling 0.5 cm in or out of the field. A typical VMAT plan requires 5000 to 10 000 beamlets, which may be calculated overnight. This results in a ternary‐valued objective function, which may use the same clinical objectives as the original VMAT plan. A simple greedy search algorithm is applied to minimize this function and determine the optimal set of ternary variables. The resulting modified control point parameters are imported into the TPS to calculate the final, deliverable dose, and to compare the EO plan with the original. EO was evaluated retrospectively on seven VMAT plans (two adult brain, one pediatric brain, two head and neck, and two prostate). Additionally, the use of stricter objectives was investigated for two of the cases: the left cochlea planning organ at risk (OAR) volume objective for the pediatric brain case, and the rectum objective for a prostate case. Results EO produced improved objective scores (by 6% to 60%) and dose‐volume histograms (DVH) for the brain plans and the head and neck plans. For each of these plans, the target dose minimum and homogeneity were preserved, while one or more of the OAR DVH’s was reduced. Although EO also reduced the objective scores for the prostate plans (by 46% and 79%), their absolute score and DVH improvements were not substantial. The stricter objective on the pediatric brain case resulted in lower dose to the OAR without compromising the target dose. However, the rectum dose in the prostate case could not be improved without reducing dose homogeneity to the planning target volume, suggesting that VMAT prostate cases may already be highly optimized by the TPS. Conclusion We have developed a novel approach to improving the dose distribution of VMAT plans, which relies on MC calculations to provide small modifications to the control points. This method may be particularly useful for complex treatments in which a certain OAR is of c...

Monte Carlo based continuous aperture optimization for VMAT on conventional and MR‐linacs: A proof of concept

“…These computation times are reasonable for a prototype platform and could be significantly reduced by moving from Matlab to compiled code and from CPU to GPU. Even as such, these total computation times are shorter than those reported by Mathews et al 18 . for their beamlet‐based method, which were of the order of 10–20 h for 5000–10000 MC beamlets simulation and similar to ours (30–90 min) for the search algorithm, again keeping in mind that they start from an already optimized plan, which MCCAO does not require.…”

“…This fact, coupled with its ability to accurately model and thus compensate for scatter and disequilibrium effects during the optimization, emphasizes the necessity of integrating MC into the actual inverse treatment planning algorithm. The MCCAO algorithm integrates MC simulations into the optimization process, generating deliverable VMAT plans, ab initio, without relying on a pre-existing plan optimized through other algorithms, as is done by Mathews et al 18 and Kontaxis et al 3 The total computation time, which includes the three optimization stages time and the four MC opti-simulations time, ranges between 40 and 120 min,for the cases presented in the Results section. These computation times are reasonable for a prototype platform and could be significantly reduced by moving from Matlab to compiled code and from CPU to GPU.…”

“…This fact, coupled with its ability to accurately model and thus compensate for scatter and disequilibrium effects during the optimization, emphasizes the necessity of integrating MC into the actual inverse treatment planning algorithm. The MCCAO algorithm integrates MC simulations into the optimization process, generating deliverable VMAT plans, ab initio, without relying on a pre‐existing plan optimized through other algorithms, as is done by Mathews et al 18 . and Kontaxis et al 3 .…”

“…Such algorithms have been previously developed by several groups, for example, Jeraj and Keall, 10 Laub et al, 11 Fippel et al, 12 Zakarian and Deasy, 13 Dogan et al, 14 Bergman et al, 15 Bogner et al, 16 Yang et al, 17 and Mathews. 18 However, none of these techniques have been developed for VMAT on an MR-linac.For the aforementioned study for delivering arc therapy on the Elekta MR-linac, 3 the arc was obtained by sequencing an existing IMRT step-and-shoot plan, not by direct optimization.…”

“…Thus, MC methods are particularly beneficial for inverse treatment planning. Such algorithms have been previously developed by several groups, for example, Jeraj and Keall, 10 Laub et al., 11 Fippel et al., 12 Zakarian and Deasy, 13 Dogan et al., 14 Bergman et al., 15 Bogner et al., 16 Yang et al., 17 and Mathews 18 . However, none of these techniques have been developed for VMAT on an MR‐linac.…”