Advancements in Monte Carlo Dose Calculations for Prostate and Breast Permanent Implant Brachytherapy

Miksys, Nelson

doi:10.22215/etd/2016-11545

Cited by 2 publications

(3 citation statements)

References 94 publications

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“…F I G U R E 8 Zoomed in crops of Figure 5a showing singular high-dose air voxels outside of the patient for noVR MC results of 177 Lu simulations and Figure 2b showing a protrusion (in red) of the prostate contour to the patient's bottom left (image's bottom right) that may not be representative of the actual prostate volume Furthermore, even using the large 80 cm side length kernel, roughly 1% of the particles (by energy) generated in the center voxel escape the DPK. Finally, roughly 22% of 177 Lu decays from the source are nonbeta decays, and for many of the possible photon decays, the change in mass-energy absorption coefficients with energy and composition 28 can further increase the discrepancy between the DPK and MC results.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, even using the large 80 cm side length kernel, roughly 1% of the particles (by energy) generated in the center voxel escape the DPK. Finally, roughly 22% of 177 Lu decays from the source are nonbeta decays, and for many of the possible photon decays, the change in mass‐energy absorption coefficients with energy and composition 28 can further increase the discrepancy between the DPK and MC results.…”

Section: Discussionmentioning

confidence: 99%

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Fast beta‐emitter Monte Carlo simulations and full patient dose calculations of targeted radionuclide therapy: introducing egs_mird

et al. 2022

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Background Targeted radionuclide therapy (TRT) is a fast‐growing field garnering much interest, with several clinical trials currently underway, that has a steady increase in development of treatment techniques. Unfortunately, within the field and many clinical trials, the dosimetry calculation techniques used remain relatively simple, often using a mix of S‐value calculations and kernel convolutions. Purpose The common TRT calculation techniques, although very quick, can often ignore important aspects of patient anatomy and radionuclide distribution, as well as the interplay there‐in. This paper introduces egs_mird, a new Monte Carlo (MC) application built in EGSnrc which allows users to model full patient tissue and density (using clinical CT images) and radionuclide distribution (using clinical PET images) for fast and detailed dose TRT calculation. Methods The novel application egs_mird is introduced along with a general outline of the structure of egs_mird simulations. The general structure of the code, and the track‐length (TL) estimator scoring implementation for variance reduction, is described. A new egs++ source class egs_internal_source, created to allow detailed patient‐wide source distribution, and a modified version of egs_radionuclide_source, changed to be able to work with egs_internal_source, are also described. The new code is compared to other MC calculations of S‐values kernels of 131I, 90Y, and 177Lu in the literature, along with further self‐validation using a histogram variant of the electron Fano test. Several full patient prostate 177Lu TRT prostate cancer treatment simulations are performed using a single set of patient DICOM CT and [18F]‐DCFPyL PET data. Results Good agreement is found between S‐value kernels calculated using egs_mird with egs_internal_source and those found in the literature. Calculating 1000 doses (individual voxel uncertainties of ∼0.05%) in a voxel grid Fano test for monoenergetic 500 keV electrons and 177Lu electrons results in 94% and 99% of the doses being within 0.1% of the expected dose, respectively. For a hypothetical 177Lu treatment, patient prostate, rectum, bone marrow, and bladder dose volume histograms (DVHs) results did not vary significantly when using the TL estimator and not modeling electron transport, modeling bone marrow explicitly (rather than using generic tissue compositions), and reducing activity to voxels containing partial or full calcifications to half or none, respectively. Dose profiles through different regions demonstrate there are some differences with model choices not seen in the DVH. Simulations using the TL estimator can be completed in under 15 min (∼30 min when using standard interaction scoring). Conclusion This work shows egs_mird to be a reliable MC code for computing TRT doses as realistically as the patient Computed Tomography (CT) and Positron Emission Tomography (PET) data allow. Furthermore, the code can compute doses to sub‐1% uncertainty within 15 min, with little to no optimization. Thus, this work supports t...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Fast beta‐emitter Monte Carlo simulations and full patient dose calculations of targeted radionuclide therapy: introducing egs_mird

et al. 2022

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“…These datasets were resampled to the same target spacing (2, 2, 2) and embedded into a 256 × 256 × 256 3D volumetric space [35]. After normalizing and window leveling [−200, 250] [36][37][38][39], to enhance the contrast and texture of soft tissue, the foreground of input voxels was selected from the background by an intersection with mask voxels images using MATLAB R2022a. To increase the amount of data for training the network, we augmented the CT images…”

Section: Patient Cohorts and Data Pre-processingmentioning

confidence: 99%

Empowering Vision Transformer by Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation

Cho,

Lee,

Kim

et al. 2023

Cancers

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U-Net, based on a deep convolutional network (CNN), has been clinically used to auto-segment normal organs, while still being limited to the planning target volume (PTV) segmentation. This work aims to address the problems in two aspects: 1) apply one of the newest network architectures such as vision transformers other than the CNN-based networks, and 2) find an appropriate combination of network hyper-parameters with reference to recently proposed nnU-Net (“no-new-Net”). VT U-Net was adopted for auto-segmenting the whole pelvis prostate PTV as it consisted of fully transformer architecture. The upgraded version (v.2) applied the nnU-Net-like hyper-parameter optimizations, which did not fully cover the transformer-oriented hyper-parameters. Thus, we tried to find a suitable combination of two key hyper-parameters (patch size and embedded dimension) for 140 CT scans throughout 4-fold cross validation. The VT U-Net v.2 with hyper-parameter tuning yielded the highest dice similarity coefficient (DSC) of 82.5 and the lowest 95% Haussdorff distance (HD95) of 3.5 on average among the seven recently proposed deep learning networks. Importantly, the nnU-Net with hyper-parameter optimization achieved competitive performance, although this was based on the convolution layers. The network hyper-parameter tuning was demonstrated to be necessary even for the newly developed architecture of vision transformers.

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Advancements in Monte Carlo Dose Calculations for Prostate and Breast Permanent Implant Brachytherapy

Cited by 2 publications

References 94 publications

Fast beta‐emitter Monte Carlo simulations and full patient dose calculations of targeted radionuclide therapy: introducing egs_mird

Fast beta‐emitter Monte Carlo simulations and full patient dose calculations of targeted radionuclide therapy: introducing egs_mird

Empowering Vision Transformer by Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation

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