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spiedigitallibrary.org
CNN-based region-of-interest image reconstruction from truncated data in cone-beam CT
We developed a region-of-interest (ROI) image reconstruction method that effectively reduces truncation artifacts in CBCT. By using U-Net-based deep learning (DL) methods, we devised a method to reduce truncation artifacts for ROI imaging. A total of 16294 image slices from 49 patient cases were used to generate projection data. The center of ...
Jul 9, 2024
CT Image Reconstruction Methods
Black-box optimization of CT acquisition and reconstruction parameters: a reinforcement learning approach
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