Fast Dcalculation in LDR brachytherapy using deep learning methods.
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Abstract
\textbf{Objective:} The Monte Carlo (MC) method provides a complete solution to the tissue heterogeneity effects in low-energy low-dose rate (LDR) brachytherapy. However, long computation times limit the clinical implementation of MC-based treatment planning solutions. This work aims to apply deep learning (DL) methods, specifically a model trained with MC simulations, to predict accurate dose to medium in medium (D\textsubscript{M,M}) distributions in LDR prostate brachytherapy.\\

\textbf{Approach:} To train the DL model, 2369 single-seed configurations, corresponding to 44 prostate patient plans, were used. These patients underwent LDR brachytherapy treatments in which $^{125}$I SelectSeed sources were implanted. For each seed configuration, the patient geometry, the MC dose volume and the single-seed plan volume were used to train a 3D Unet convolutional neural network. Previous knowledge was included in the network as an r\textsuperscript{2} kernel related to the first-order dose dependency in brachytherapy. MC and DL dose distributions were compared through the dose maps, isodose lines, and dose-volume histograms. Features enclosed in the model were visualized.\\

\textbf{Main results:} Model features started from the symmetrical kernel and finalized with an anisotropic representation that considered the patient organs and their interfaces, the source position, and the low- and high-dose regions. For a full prostate patient, small differences were seen below the 20\% isodose line. When comparing DL-based and MC-based calculations, the predicted CTV D\textsubscript{90} metric had an average difference of -0.1\%. Average differences for OARs were -1.3\%, 0.07\%, and 4.9\% for the rectum D\textsubscript{2cc}, the bladder D\textsubscript{2cc}, and the urethra D\textsubscript{0.1cc}. The model took 1.8 ms to predict a complete 3D D\textsubscript{M,M} volume (1.18 M voxels).\\

\textbf{Significance:} The proposed DL model stands for a simple and fast engine which includes prior physics knowledge of the problem. Such an engine considers the anisotropy of a brachytherapy source and the patient tissue composition.© 2023 Institute of Physics and Engineering in Medicine.