Learning-based motion artifact removal networks for quantitative R 2 ∗ mapping.

Xiaojian Xu,Satya V V N Kothapalli,Jiaming Liu,Sayan Kahali,Weijie Gan,Dmitriy A Yablonskiy,Ulugbek S Kamilov

Magnetic resonance in medicine（2022）

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摘要

Both LEARN-IMG and LEARN-BIO can enable the computation of high-quality motion- and -inhomogeneity-corrected maps. LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of maps, while LEARN-BIO directly performs motion- and -inhomogeneity-corrected estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN-BIO is an advantage that can lead to a broader clinical application.

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关键词

mapping,MRI,convolutional neural networks,deep learning,gradient recalled echo,motion correction,self-supervised deep learning

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