Low-dose CT image denoising using residual convolutional network with fractional TV loss

Abstract

In this work, we propose a Fractional-order Residual Convolutional Neural Network (FRCNN) for Low-Dose CT (LDCT) denoising. As increasing the dose of radiation is harmful to the patient, how to trade off between reducing the radiation dose and improving the quality of the CT image has become a challenging problem. To this end, this paper proposes a new approach for LDCT image denoising using Convolutional Neural Network (CNN) with Fractional-order Total Variation (FTV) loss, as well as residual learning. Firstly, this paper introduced the FTV loss function for structural details enhancement. Secondly, skip connections were added to optimize the network. Thirdly, extensive experimental analysis was used to evaluate the capacity of this method in suppressing noise and preserving detailed information. The FTV loss can retain essential structural details while suppressing noise, generating high-quality CT images ready for interpretation by radiologists. Compared with state-of-the-art methods, our method obtained better results visually and numerically, especially in structural details preservation. These promising results will significantly improve the usability of LDCT images.