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. Wu^2,3, 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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