Escape the Corrupted Lines by a Learning Sampling Pattern for MRI Reconstruction
Authors: 
Jingyi Ren, Kun Shang, Qiyu Jin, Caiying Wu, Dong LiangAuthors Info & Claims
Article No.: 53, Pages 1 - 6
Abstract
Magnetic Resonance Imaging (MRI) is a powerful medical imaging technique in clinical practice. Unfortunately, the long scan times of itself and the patient’s uncontrollable motion result in image artifacts, and increase the operational costs. In this work, we propose an end-to-end joint learning framework of sampling and reconstruction, which can select the un-corrupted lines as many as possible to improve the robustness of the reconstruction module to motion by a learnable Gaussian mixture module. Experimental evaluations on the fastMRI knee dataset with simulated motion demonstrate that our method is efficient in achieving high-quality reconstruction and correcting artifacts.
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Published In
April 2024
381 pages
ISBN:9798400716393
DOI:10.1145/3661725
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Published: 20 June 2024
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CMLDS 2024
CMLDS 2024: 2024 International Conference on Computing, Machine Learning and Data Science
April 12 - 14, 2024
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