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Recently, various deep learning (DL)-based frameworks have been proposed for predicting dose distributions for radiotherapy treatment, which serve as initial dose maps and provide treatment planners with optimization starting points. This can be advantageous for dynamic treatment sites such as pancreatic cancer due to large patient to patient anatomic variations. DL-based methods thus far have used CT and contour maps to train DL networks for predictions of dose distributions. While these inputs provide important information such as density variations within a CT slice as well as spatial locations of various structures within the CT slice, these inputs do not provide radiation dose deposition information, which is typically used in inverse optimization algorithms during treatment planning to reach the desired dose distribution. In this work, we propose a new deep learning-based correction method for generating modulated dose map from non-modulated dose map, which is the dose distribution achieved from uniform beamlet intensities. Our method is consisted of two subnetworks: fusion module and correction module. Fusion module extracts features from CT and contours, including GTV contour and multi-OAR contours. Through hierarchical layers, the extracted feature map is represented as a target activation map, which can well represent the distribution of modulated dose map. To include the geometric information, the target activation map together with non-modulated dose map are then transferred into the correction module to obtain the estimated modulated dose map. Histogram matching loss with traditional losses are used. The goal of histogram matching loss is used to match the distribution of estimated modulated dose to that of ground truth modulated dose map. We performed 3-fold cross-validation on a dataset consisted of 30 patients. Our proposed method generated comparable predictions, compared to the ground truth, for 20/23 clinically relevant dose volume parameters. Overall results demonstrate the feasibility and efficacy of our proposed DL-based method for pancreas SBRT dose prediction.
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