Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI

Magnetic Resonance Imaging (MRI) is sensitive to motion caused by patient movement due to the relatively long data acquisition time. This could cause severe degradation of image quality and therefore affect the overall diagnosis. In this paper, we develop an efficient retrospective 2D deep learning...

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Auteurs principaux:	Mohammed A. Al-masni, Seul Lee, Jaeuk Yi, Sewook Kim, Sung-Min Gho, Young Hun Choi, Dong-Hyun Kim
Format:	Artigo
Langue:	Inglês
Publié:	Elsevier 2022-10-01
Collection:	NeuroImage
Sujets:	Deep learning Motion artifact correction MRI Prior-assisted Stacked U-Nets
Accès en ligne:	http://www.sciencedirect.com/science/article/pii/S1053811922005286
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