Abstract

Info

Title:: Metal artifact reduction in cervical CT images using convolutional neural network

Author(s):: HUANG Xia1; XU Yikai1; ZHANG Yu2; 3; 1. Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China 2. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China 3. Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China

Keywords:: Keywords: metal artifact data simulation convolutional neural network cervical CT image

Abstract:: Abstract: Objective To reduce metal artifacts in cervical CT images using convolutional neural network. Methods The metal artifact images and the target images (artifact-free images) were generated using numerical simulation for constructing training and test data sets. The cervical CT images with metal artifacts and paired cervical CT images without metal artifacts were input into the constructed convolutional neural network for training, and then a convolutional neural network model for metal artifact reduction in cervical CT images was obtained. Results Before network training, the average peak signal-to-noise ratio (PSNR) of the metal artifact images and the target images was 26.098 0 dB. The average PSNR of the metal artifact reduction images and the target images obtained by the training network trained by image patches of different sizes (25×25, 50×50, 100×100) was 34.607 9, 38.375 1, and 38.183 8 dB, respectively. Conclusion Through experiments on simulation data and clinical data, it is revealed that the proposed method can effectively reduce metal artifacts and can retain relatively complete tissue texture information in cervical CT images.