U-Net Transfer Learning for Image Restoration on Sparse CT Reconstruction in Pre-Clinical Research

Abstract

Sparse computed tomography (CT) reconstruction can lead to significant streak artifacts. Image restoration that removes these artifacts while recovering image features is an important area of research in low-dose sparse CT imaging. In pre-clinical research, where a lag still exists in the use of professional CT equipment, existing imaging devices provide limited X-ray dose energy accompanied by strong noise patterns when scanning. Reconstructed CT images contain significant noise and artifacts. We propose a deep transfer learning (DTL) neural network training method that exploits open-source data for initial training and a small-scale detected phantom image with its total variation result for transfer learning to address this issue. We hypothesize that a pre-trained neural network from open-source data has no prior knowledge of our device configuration, which prevents its application on our measured data, and deep transfer learning on small-scale detected phantom can feed specific configurations into the model. Our experiment has demonstrated that our proposed method, incorporating a modified total variation (TV) algorithm, can successfully realize a good balance between artifact removal and image feature restoration.