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Residual U-Net Convolutional Neural Network Architecture for Low-Dose CT Denoising
2018
Current Directions in Biomedical Engineering
Low-dose CT has received increasing attention in the recent years and is considered a promising method to reduce the risk of cancer in patients. However, the reduction of the dosage leads to quantum noise in the raw data, which is carried on in the reconstructed images. Two different multilayer convolutional neural network (CNN) architectures for the denoising of CT images are investigated. ResFCN is based on a fully-convolutional network that consists of three blocks of 5×5 convolutions
doi:10.1515/cdbme-2018-0072
fatcat:wopz2vgafrhjro5r5huok6pezi