A method of rapid quantification of patient‐specific organ doses for CT using deep‐learning‐based multi‐organ segmentation and GPU‐accelerated Monte Carlo dose computing

Abstract

Purpose: The ability to obtain patient-specific organ doses for CT will open the door to new applications such as personalized selection of scan factors and individualized risk assessment, leading to the ultimate goal of achieving lowdose and optimized CT imaging. One technical barrier to advancing CT dosimetry has been the lack of computational tools for automatic patient-specific multi-organ segmentation of CT images, coupled with rapid organ dose quantification.This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of radiosensitive organs from CT images and GPU-based Monte Carlo rapid organ dose calculation. Methods: A deep convolutional neural network (CNN) based on the U-Net for organ segmentation is developed and trained to automatically delineate radiosensitive organs from CT images. Two databases are used: the Lung CT Segmentation Challenge 2017 (LCTSC) dataset that contains 60 thoracic CT scan patients each with 5 segmented organs, and the Pancreas-CT (PCT) dataset that contains 43 abdominal CT scan patients each with 8 segmented organs. A five-fold cross-validation of the new method is performed on both sets of data. Dice Similarity Coefficient (DSC) is used to evaluate the segmentation performance against the ground truth. A GPU-based Monte Carlo dose code, ARCHER, is used to calculate patient-specific CT organ doses. The proposed method is tested in terms of Relative Dose Error (RDE). To demonstrate the potential improvement of the new methods, organ dose results are compared against those obtained for population-average phantoms used in an off-line dose reporting software, VirtualDose, at Massachusetts General Hospital. Results: For the group of 60 patients from LCTSC dataset, the median DSCs are found to be 0.97 (right lung), 0.96 (left lung), 0.93 (heart), 0.88 (spinal cord) and 0.78 (esophagus). For the group of 43 patients from PCT dataset, the median DSCs are found to be 0.96 (spleen), 0.96 (liver), 0.95 (left kidney), 0.89 (stomach), 0.87 (gall bladder), 0.79(pancreas), 0.74 (esophagus), and 0.64 (duodenum). Comparing with the organ dose results from population-averaged phantoms, the new patient-specific method achieved the smaller RDE range on all organs: -4.3%~1.5% (vs -31.5%~33.9%) for the lung, -7.0%~2.3% (vs -15.2%~125.1%) for the heart, -18.8%~40.2% (vs -10.3%~124.1%) for the esophagus, -5.6%~1.6% (vs -20.3%~57.4%) for the spleen, -4.5%~4.6% (vs -19.5%~61.0%) for the pancreas, -2.3%~4.4% (vs -37.8%~75.8%) for the left kidney, -14.9%~5.4% (vs -39.9% ~14.6%) for the gallbladder, -0.9%~1.6%(vs -30.1%~72.5%) for the liver, and -23.0%~11.1% (vs -52.5%~-1.3%) for the stomach. The trained automatic segmentation tool takes less than 5 seconds in a patient for all 103 patients in the dataset. The Monte Carlo radiation dose calculations performed in parallel with the segmentation using the GPU-accelerated ARCHER code takes less than 4 seconds in a patient to achieve <0.5% statistical uncertainty in all organ doses for all 103 patients in the...

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