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Abstract #0429

Gibbs-Ringing Artifact Reduction in MRI via Machine Learning Using Convolutional Neural Network

Qianqian Zhang1, Guohui Ruan1, Wei Yang1, Kaixuan Zhao1, Ed X. Wu2,3, and Yanqiu Feng1

1Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 2Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, 3Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China

The Gibbs-ringing artifact is caused by the insufficient sampling of the high frequency data. Existing methods generally exploit smooth constraints to reduce intensity oscillations near high-contrast boundaries but at the cost of blurring details. This work presents a convolutional neural network (CNN) method that maps ringing images to their ringing-free counterparts for Gibbs-ringing artifact removal in MRI. The experimental results demonstrate that the proposed method can effectively remove Gibbs-ringing without introducing noticeable blurring.

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