A new DOSXYZnrc method for Monte Carlo simulations of 4D dose distributions

Su, Shiqin; Atwal, P; Lobo, Julio; Duzenli, Cheryl; Popescu, I. Antoniu

doi:10.1088/1361-6560/ac3a24

Cited by 2 publications

(5 citation statements)

References 28 publications

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“…The algorithm uses a dose–volume cost function that includes the MC statistical uncertainty of each voxel, effectively reducing the contribution of voxels with high statistical uncertainty:

\begin{equation} Cost = \sum _{j=1}^{O}\sum _{i=1}^{n(j)}{{\frac{p(j)}{n(j)}}{\frac{1}{\delta D_{i,MC}}}(D_{i,MC}-D_{i,goal})^2} \end{equation}

where O is the total number of optimization objectives,

p(j)

and

n(j)

are the priority and number of voxels in the structure for the objective j ,

\delta D_{i,MC}

and

D_{i,MC}

are the relative statistical uncertainty and the current dose in voxel i , and

D_{i,goal}

is the dose constraint used in the optimizer. The calculation of

\delta D_{i,MC}

was presented in Su et al 19 . Changes that lead to reduced cost are accepted and cost value, dose distribution, statistical uncertainty, and DVH are updated accordingly.…”

Section: Methodsmentioning

confidence: 99%

“…25,26 The MC models used in this paper have been previously extensively validated. 27 The relevant details of our simulation apparatus and the modifications necessary to obtain 4D dose distributions, which are required by the optimization algorithm, are described in Su et al 19 First, a 4D phase space is scored in BEAMnrc on the top surface of the SYNCVMLC or SYNCHDMLC component modules. The size of this phase space depends on field size.…”

Section: Monte Carlo Simulation Detailsmentioning

confidence: 99%

“…The 4D energy deposition data are recorded in edepheader and edepdat files, which contain individual, time-mapped, energy deposition events and are read by the MCCAO algorithm. This process and the format of these files are described in detail in Su et al 19 The file size of edepheader and edepdat is related to the number of histories simulated and the extent of the VOI (e.g., about 8 GB for a simulation using 500 million histories and involving a VOI of 1000 cm 3 ). These files are generated in the course of a DOSXYZnrc simulation, without any increase in computation time.…”

Section: The Mccao Workflowmentioning

confidence: 99%

“…where O is the total number of optimization objectives, p(j) and n(j) are the priority and number of voxels in the structure for the objective j, 𝛿D i,MC and D i,MC are the relative statistical uncertainty and the current dose in voxel i, and D i,goal is the dose constraint used in the optimizer. The calculation of 𝛿D i,MC was presented in Su et al 19 Changes that lead to reduced cost are accepted and cost value, dose distribution, statistical uncertainty, and DVH are updated accordingly. The MCCAO algorithm is able to generate plans with or without jaw tracking.…”

Section: The Mccao Workflowmentioning

confidence: 99%

“…In our previous publication,19 we briefly mentioned the algorithm presented here as "MC-DAO,"where that acronym referenced "direct aperture optimization." However, as all previously published DAO-based algorithms involved discrete optimization, we wanted to underline the fact that our approach involves continuous beam modeling at all stages of the optimization.…”

mentioning

confidence: 99%

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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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“…The algorithm uses a dose–volume cost function that includes the MC statistical uncertainty of each voxel, effectively reducing the contribution of voxels with high statistical uncertainty:

\begin{equation} Cost = \sum _{j=1}^{O}\sum _{i=1}^{n(j)}{{\frac{p(j)}{n(j)}}{\frac{1}{\delta D_{i,MC}}}(D_{i,MC}-D_{i,goal})^2} \end{equation}

where O is the total number of optimization objectives,

p(j)

and

n(j)

are the priority and number of voxels in the structure for the objective j ,

\delta D_{i,MC}

and

D_{i,MC}

are the relative statistical uncertainty and the current dose in voxel i , and

D_{i,goal}

is the dose constraint used in the optimizer. The calculation of

\delta D_{i,MC}

was presented in Su et al 19 . Changes that lead to reduced cost are accepted and cost value, dose distribution, statistical uncertainty, and DVH are updated accordingly.…”

Section: Methodsmentioning

confidence: 99%