Abstract #1197

A deep-learning model for effective ringing artifact removal by developing a novel multi-frequency Gibbs generator algorithm

Lisong Dai¹, Zhenzhuang Miao², Lei Lu², Yuting Ling³, Hongyu Guo², Xiaoyun Liang³, Qin Xu², and Yuehua Li¹

¹Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, ²MRI R&D, Neusoft Medical Systems Co. Ltd., Shanghai, China, ³Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Shanghai, China

Synopsis

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, multi-frequency Gibbs artifact, deep learning, model training, artifact removal

Motivation: Gibbs artifact generated by zero-padding k-space data for model training poses a huge challenge for the model to learn different severity and manifestation of Gibbs artifact in the image domain.

Goal(s): Our goal was to effectively remove ringing artifact with a deep-learning model by developing a novel multi-frequency Gibbs generator algorithm.

Approach: We introduced Gibbs artifact generator (GAG) algorithm to create Gibbs artifacts with different truncation ratios as the input and tested the performance with a proposed deep-learning model.

Results: The images processed using the proposed approach demonstrated higher image quality score than the original images (all P < 0.05).

Impact: The images generated by our new GAG algorithm with pronounced multi-frequency Gibbs artifacts could be used as a reliable training set for deep-learning model training, enabling the model to effectively identify and eliminate Gibbs artifacts in spinal MR imaging.

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