Concurrent Monte Carlo transport and fluence optimization with fluence adjusting scalable transport Monte Carlo

Yang, Yanan; Svatos, M; Zankowski, Corey; Bednarz, Bryan

doi:10.1118/1.4950711

Cited by 3 publications

(5 citation statements)

References 37 publications

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“…Instead of computing each individual beamlet dose distribution, the dose is directly accumulated into a total distribution. Yang et al 13 . developed a similar method based on concurrent Monte Carlo simulation and fluence optimization for photon radiotherapy.…”

Section: Methodsmentioning

confidence: 99%

“…Instead of computing each individual beamlet dose distribution, the dose is directly accumulated into a total distribution. Yang et al 13 developed a similar method based on concurrent Monte Carlo simulation and fluence optimization for photon radiotherapy. Their method consists of dividing beamlets in sub-beamlets and the consecutive simulation of a small number of particles, typically between 10 and 1000, in each iteration for each sub-beamlet.…”

Section: Beamlet-free Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Technical note: Beamlet‐free optimization for Monte‐Carlo‐based treatment planning in proton therapy

Pross,

Wuyckens,

Deffet

et al. 2023

Medical Physics

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BackgroundDose calculation and optimization algorithms in proton therapy treatment planning often have high computational requirements regarding time and memory. This can hinder the implementation of efficient workflows in clinics and prevent the use of new, elaborate treatment techniques aiming to improve clinical outcomes like robust optimization, arc, and adaptive proton therapy.PurposeA new method, namely, the beamlet‐free algorithm, is presented to address the aforementioned issue by combining Monte Carlo dose calculation and optimization into a single algorithm and omitting the calculation of the time‐consuming and costly dose influence matrix.MethodsThe beamlet‐free algorithm simulates the dose in proton batches of randomly chosen spots and evaluates their relative impact on the objective function at each iteration. Based on the approximated gradient, the spot weights are then updated and used to generate a new spot probability distribution. The beamlet‐free method is compared against a conventional, beamlet‐based treatment planning algorithm on a brain case and a prostate case.ResultsThe beamlet‐free algorithm maintained a comparable plan quality while largely reducing the dependence of computation time and memory usage on the number of spots.ConclusionThe implementation of a beamlet‐free treatment planning algorithm for proton therapy is feasible and capable of achieving treatment plans of comparable quality to conventional methods. Its efficient usage of computational resources and low spot dependence makes it a promising method for large plans, robust optimization, and arc proton therapy.

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Section: Methodsmentioning

confidence: 99%

Section: Beamlet-free Algorithmmentioning

confidence: 99%

Technical note: Beamlet‐free optimization for Monte‐Carlo‐based treatment planning in proton therapy

Pross,

Wuyckens,

Deffet

et al. 2023

Medical Physics

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“…Geant4 is a versatile object-oriented simulation toolkit that allows for the modeling of complex geometries, radiation sources, and detectors. Geant4 has been benchmarked for a variety of different medical physics applications (Faddegon et al 2009, Bednarz et al 2010, Titt et al 2012, Besemer et al 2013, Yang and Bednarz 2013, Yang et al 2016). The CT and PET images were used in the MC simulation to define the geometry and source distribution, respectively.…”

Section: Methodsmentioning

confidence: 99%

Murine-specific Internal Dosimetry for Preclinical Investigations of Imaging and Therapeutic Agents

Bednarz¹,

Grudzinski²,

Marsh³

et al. 2018

Health Physics

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There is a growing need to estimate the absorbed dose to small animals from preclinical investigations involving diagnostic and therapeutic radiopharmaceuticals. This paper introduces a Monte Carlo-based dosimetry platform called RAPID, which is capable of calculating murine-specific three-dimensional (3D) dose distributions. A comparison is performed between absorbed doses calculated with RAPID and absorbed doses calculated in a commonly used reference mouse phantom called MOBY. Four test mice containing different xenografts underwent serial PET/CT imaging using a novel diagnostic therapy (theranostic) agent NM404, which can be labeled with I for imaging or I for therapy. Using the PET/CT data, 3D dose distributions from I-NM404 were calculated in the mice using RAPID. Mean organ doses in these four test mice were compared to mean organ doses derived by using two previously published I S-values datasets in MOBY. In addition, mean tumor doses calculated in RAPID were compared to mean organ doses derived from unit density spheres. Large differences were identified between mean organ doses calculated in the test mice using RAPID and those derived in the MOBY phantom. Mean absorbed dose percent errors in organs ranged between 0.3% and 333%. Overall, mass scaling improved agreement between MOBY phantom calculations and RAPID, where percent errors were all less than 26%, with the exception of the lung in which percent errors reached values of 48%. Percent errors in mean tumor doses in the test mice and unit density spheres were less pronounced but still ranged between 8% and 23%. This work demonstrates the limitations of using pre-computed S-values in computational phantoms to predict organ doses in small animals from theranostic procedures. RAPID can generate accurate 3D dose distributions in small animals and in turn offer much greater insight on the ability of a given theranostic agent to image and treat diseases.

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“…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.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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Atwal

Popescu

2023

Medical Physics

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Background: Currently, the commercial treatment planning systems for magnetic-resonance guided linear accelerators (MR-linacs) only support step-and-shoot intensity-modulated radiation therapy (IMRT). However, recent studies have shown the feasibility of delivering arc therapy on MR-linacs, which is expected to improve dose distributions and delivery speed. By accurately accounting for the electron return effect in the presence of a magnetic field, a Monte Carlo (MC) algorithm is ideally suited for the inverse treatment planning of this technique. Purpose: We propose a novel MC-based continuous aperture optimization (MCCAO) algorithm for volumetric modulated arc therapy (VMAT), including applications to VMAT on MR-linacs and trajectory-based VMAT. A unique feature of MCCAO is that the continuous character of gantry rotation and multileaf collimator (MLC) motion is accounted for at every stage of the optimization. Methods: The optimization process uses a multistage simulation of 4D dose distribution. A phase space is scored at the top surface of the MLC and the energy deposition of each particle history is mapped to its position in this phase space. A progressive sampling method is used, where both MLC leaf positions and monitor unit (MU) weights are randomly changed, while respecting the linac mechanical limits. Due to the continuous nature of the leaf motion, such changes affect not only a single control point,but propagate to the adjacent ones as well, and the corresponding dose distribution changes are accounted for. A dose-volume cost function is used,which includes the MC statistical uncertainty. Results: We applied our optimization technique to various treatment sites,using standard and flattening-filter-free (FFF) 6 MV beam models, with and without a 1.5 T magnetic field. MCCAO generates deliverable plans, whose dose distributions are in good agreement with measurements on ArcCHECK and stereotactic radiosurgery End-To-End Phantom. Conclusions: We show that the novel MCCAO method generates VMAT plans that meet clinical objectives for both conventional and MR-linacs.

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Concurrent Monte Carlo transport and fluence optimization with fluence adjusting scalable transport Monte Carlo

Abstract: This study demonstrates a fluence optimization approach, which could significantly improve the development of next generation radiation therapy solutions while incurring minimal additional computational overhead.

Cited by 3 publications

References 37 publications

Technical note: Beamlet‐free optimization for Monte‐Carlo‐based treatment planning in proton therapy

Technical note: Beamlet‐free optimization for Monte‐Carlo‐based treatment planning in proton therapy

Murine-specific Internal Dosimetry for Preclinical Investigations of Imaging and Therapeutic Agents

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

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