An Approach in Radiation Therapy Treatment Planning: A Fast, GPU-Based Monte Carlo Method

Karbalaee, Mojtaba; Shahbazi‐Gahrouei, Daryoush; Tavakoli, Mahdi

doi:10.4103/2228-7477.205507

Cited by 11 publications

(1 citation statement)

References 26 publications

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“…However, these calculations were modeled using an un-optim ised MC code and the calculation time could certainly be considerably reduced using (e.g.) a GPUbased fast MC (Jia et al 2012, Mojtaba et al 2017. In addition, we are currently working on an analytical method of simulating the RIDDCs.…”

Section: Discussionmentioning

confidence: 99%

Positioning of head and neck patients for proton therapy using proton range probes: a proof of concept study

et al. 2017

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To exploit the full potential of proton therapy, accurate and on-line methods to verify the patient positioning and the proton range during the treatment are desirable. Here we propose and validate an innovative technique for determining patient misalignment uncertainties through the use of a small number of low dose, carefully selected proton pencil beams ('range probes') (RP) with sufficient energy that their residual Bragg peak (BP) position and shape can be measured on exit. Since any change of the patient orientation in relation to these beams will result in changes of the density heterogeneities through which they pass, our hypothesis is that patient misalignments can be deduced from measured changes in Bragg curve (BC) shape and range. As such, a simple and robust methodology has been developed that estimates average proton range and range dilution of the detected residual BC, in order to locate range probe positions with optimal prediction power for detecting misalignments. The validation of this RP based approach has been split into two phases. First we retrospectively investigate its potential to detect translational patient misalignments under real clinical conditions. Second, we test it for determining rotational errors of an anthropomorphic phantom that was systematically rotated using an in-house developed high precision motion stage. Simulations of RPs in these two scenarios show that this approach could potentially predict translational errors to lower than1.5 mm and rotational errors to smaller than 1° using only three or five RPs positions respectively.

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Section: Discussionmentioning

confidence: 99%

Positioning of head and neck patients for proton therapy using proton range probes: a proof of concept study

et al. 2017

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Whole-body voxel-based internal dosimetry using deep learning

Akhavanallaf

Shiri

Arabi

et al. 2020

Eur J Nucl Med Mol Imaging

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Purpose In the era of precision medicine, patient-specific dose calculation using Monte Carlo (MC) simulations is deemed the gold standard technique for risk-benefit analysis of radiation hazards and correlation with patient outcome. Hence, we propose a novel method to perform whole-body personalized organ-level dosimetry taking into account the heterogeneity of activity distribution, non-uniformity of surrounding medium, and patient-specific anatomy using deep learning algorithms. Methods We extended the voxel-scale MIRD approach from single S-value kernel to specific S-value kernels corresponding to patient-specific anatomy to construct 3D dose maps using hybrid emission/transmission image sets. In this context, we employed a Deep Neural Network (DNN) to predict the distribution of deposited energy, representing specific S-values, from a single source in the center of a 3D kernel composed of human body geometry. The training dataset consists of density maps obtained from CT images and the reference voxelwise S-values generated using Monte Carlo simulations. Accordingly, specific S-value kernels are inferred from the trained model and whole-body dose maps constructed in a manner analogous to the voxel-based MIRD formalism, i.e., convolving specific voxel S-values with the activity map. The dose map predicted using the DNN was compared with the reference generated using MC simulations and two MIRD-based methods, including Single and Multiple S-Values (SSV and MSV) and Olinda/EXM software package. Results The predicted specific voxel S-value kernels exhibited good agreement with the MC-based kernels serving as reference with a mean relative absolute error (MRAE) of 4.5 ± 1.8 (%). Bland and Altman analysis showed the lowest dose bias (2.6%) and smallest variance (CI: − 6.6, + 1.3) for DNN. The MRAE of estimated absorbed dose between DNN, MSV, and SSV with respect to the MC simulation reference were 2.6%, 3%, and 49%, respectively. In organ-level dosimetry, the MRAE between the proposed method and MSV, SSV, and Olinda/EXM were 5.1%, 21.8%, and 23.5%, respectively. Conclusion The proposed DNN-based WB internal dosimetry exhibited comparable performance to the direct Monte Carlo approach while overcoming the limitations of conventional dosimetry techniques in nuclear medicine.

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RETRACTED ARTICLE: Computer electromagnetic radiation carcinogenic doses based on Monte Carlo algorithm

Hou

Wang

et al. 2018

Cluster Comput

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An Approach in Radiation Therapy Treatment Planning: A Fast, GPU-Based Monte Carlo Method

Cited by 11 publications

References 26 publications

Positioning of head and neck patients for proton therapy using proton range probes: a proof of concept study

Positioning of head and neck patients for proton therapy using proton range probes: a proof of concept study

Whole-body voxel-based internal dosimetry using deep learning

RETRACTED ARTICLE: Computer electromagnetic radiation carcinogenic doses based on Monte Carlo algorithm

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