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Deep Learning in MR Motion Correction: a Brief Review and a New Motion Simulation Tool (view2Dmotion)

김동현|Seul Lee|Soozy Jung|Kyu-Jin Jung
대한자기공명의과학회 (Korean Society Of Magnetic Resonance In Medicine)|2020.12|Investigative Magnetic Resonance Imaging|제24권 제4호|pp. 196-206

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키워드

Machine learningDeep learningMotion artifactMotion correctionMotion simulation
주제분류방사선과학

초록

With the development of deep-learning techniques, the application of deep learning in MR imaging processing seems to be growing. Accordingly, deep learning has also been introduced in motion correction and seemed to work as well as do conventional motion-compensation methods. In this article, we review the motion-correction methods based on deep learning, focusing especially on the motion-simulation methods adopted. We then propose a new motion-simulation tool, which we call view2Dmotion.

Abstract

With the development of deep-learning techniques, the application of deep learning in MR imaging processing seems to be growing. Accordingly, deep learning has also been introduced in motion correction and seemed to work as well as do conventional motion-compensation methods. In this article, we review the motion-correction methods based on deep learning, focusing especially on the motion-simulation methods adopted. We then propose a new motion-simulation tool, which we call view2Dmotion.

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