Abstract

Info

Title:: Application of adversarial training-based U-Net neural network in the enhancement of CT images obtained by sparse projection

Author(s):: HUANG Jinwei1; XIAO Wenpeng2; ZHU Siting3; QIU Haoyi1; CHEN Xingyu2; LIU Shenquan1; 1. School of Mathematics, South China University of Technology, Guangzhou 5106401, China 2. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China 3. School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China

Keywords:: CT image enhancement U-Net neural network adversarial training sparse projection

Abstract:: Objective For the existence of noise and artifacts in reconstructed CT images obtained by sparse projection, a neural network model is proposed for the enhancement of low-quality CT images obtained by sparse projection. Methods Based on residual encoder-decoder convolutional neural network, an adversarial training-based U-Net neural network was proposed. Model training and testing were carried out using the CT images of cancer from TCGA-CESC dataset. The main indicators for the evaluation of the model processing results included peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and root-mean-square error (RMSE). Results In the test on the reconstructed CT images obtained by 180 detections, the average values of the PSNR, SSIM and RMSE of the images processed by the proposed model were increased by 15.10%, 37.89% and 38.20%, respectively, compared with those of the unprocessed images. Moreover, the average values of PSNR and SSIM indicated that the processed images were superior to the unprocessed images obtained by 1 800 detections. Conclusion The proposed neural network model can reduce artifacts and noise, and has certain effects on the enhancement of CT images obtained by sparse projection.