Cascade ResUnet with Noise Power Spectrum Loss for Low Dose CT Imaging

The suppression of noise-artifacts in computed tomography (CT) with a low dose scan protocol is a well-known challenge. Conventional statistical iterative algorithms are able to provide improved reconstructions, but do not work well at eliminating large streaks and strong noise. In this paper, we present a 3D cascade ResUnet neural network (Ca-ResUnet) strategy with modified noise power spectrum loss for the reduction of noise-artifacts in low dose CT reconstruction. The reconstruction workflow consists of four components: the first component is FBP (filtered backprojection) reconstruction module, the second is a ResUnet neural network with operating in the CT image, the third is image update module to compensated the loss of tiny structure, and the last is a ResUnet neural network with modified noise power spectrum loss for reconstruction image fine tuning. Verification results based on AAPM clinical data confirm that the proposed method can significantly reduce serious noise-artifacts.