Meeting Banner
Abstract #4856

Gibbs-Ringing Artifact reduction in MR images with varying sampling levels Via a Single Convolutional Neural Network

Guohui Ruan1, Qianqian Zhang1, Biaoshui Liu2, Wei Yang1, Yingjie Mei3, Ed X. Wu4, and Yanqiu Feng1

1Guangdong Provincial Key Laboratory of Medical Image Processing & Key Laboratory of Mental Health of the Ministry of Education, School of Biomedical Engineering, Southern Medical University, Guangzhou, China, Guangzhou, China, 2Sun Yat-Sen University Cancer Center, Guangzhou, China, 3Philips Healthcare, Guangzhou, China, Guangzhou, China, 4Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China, Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, Hong Kong, China

Gibbs-ringing artifact is caused by the insufficient sampling of the high frequency data. And in clinical practice, the appearance of ringing artifact, i.e. the real sampling level, is not accurately obtained. To address this problem, a single convolutional neural network (CNN) has been trained for reducing Gibbs-ringing artifact in MR images under varying sampling levels. The experimental results demonstrate that Gibbs-ringing artifact can be effectively reduced by the proposed method without introducing noticeable blurring.

This abstract and the presentation materials are available to members only; a login is required.

Join Here