Low-Dose CT Post-processing Based on 2D Residual Network

Low-dose CT is an effective solution to alleviate radiation risk to patients; it also introduces additional noise and streak artifacts. In order to maintain a high image quality for low-dose scanned CT data, we propose a post-processing method based on deep learning and using 2D residual convolutional networks. Experimental results and comparisons with other competing methods show that the proposed approach can effectively reduce the low-dose noise and artifacts while preserving tissue details. Factors that may influence the model performance, such as model width, depth, and dropout, are also examined.

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