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Abstract #5002

Deep-learning-based flow-artifact correction for multi-shot multiple overlapping-echo detachment imaging (msh-MOLED)

Ying Lin1, Qizhi Yang1, Ming Ye1, Jianfeng Bao2, Zhong Chen1, Liangjie Lin3, Congbo Cai1, and Shuhui Cai1
1Xiamen University, Xiamen, China, 2Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, 3MSC Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China

Synopsis

Keywords: Artifacts, Data Acquisition, Image Reconstruction

Motivation: Multi-shot overlapping-echo detachment imaging (msh-MOLED), a msh-EPI-based quantitative MR sequence, quantifies tissue T2 rapidly without the need of separately acquiring images with different TEs, but its results could be contaminated by flow-induced inter-shot phase variations.

Goal(s): To implement an instantaneous referenceless flow-artifact correction for msh-MOLED.

Approach: Flow-related features were added to the training data, and the trained network fulfilled T2 mapping free from flow artifacts without dear computational costs or additional reference data.

Results: After correction, the Pearson’s correlation coefficient/mean absolute error was changed from 0.6332/6.5328 (uncorrected) to 0.8808/2.7623 (corrected).

Impact: The proposed correction could be used to retain the mapping accuracy of msh-MOLED regardless of shot numbers, or to refine the reference data in high-spatial-resolution diffusion mapping potentially.

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Keywords