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.

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