Correcting motion artifacts in MRI scans using a deep neural network with automatic motion timing detection

Michael Rotman; Rafi Brada; Israel Beniaminy; Sangtae Ahn; Christopher J. Hardy; Lior Wolf

doi:10.1117/12.2580869

15 February 2021 Correcting motion artifacts in MRI scans using a deep neural network with automatic motion timing detection

Michael Rotman, Rafi Brada, Israel Beniaminy, Sangtae Ahn, Christopher J. Hardy, Lior Wolf

Author Affiliations +

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 1159514 (2021) https://doi.org/10.1117/12.2580869
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Motion artefacts created by patient motion during an MRI scan occur frequently in practice, often rendering the scans clinically unusable and requiring a re-scan. While many methods have been employed to ameliorate the effects of patient motion, these often fall short in practice. In this paper we propose a novel method for detecting and timing patient motion during an MR scan and correcting for the motion artefacts using a deep neural network. The deep neural network contains two input branches that discriminate between patient poses using the motion’s timing. The first branch receives a subset of the k-space data collected during a single dominant patient pose, and the second branch receives the remaining part of the collected k-space data. The proposed method can be applied to artefacts generated by multiple movements of the patient. Furthermore, it can be used to correct motion for the case where k-space has been under-sampled to shorten the scan time, as is common when using methods such as parallel imaging or compressed sensing. Experimental results on both simulated and real MRI data show the efficacy of our approach.

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Citation Download Citation

Michael Rotman, Rafi Brada, Israel Beniaminy, Sangtae Ahn, Christopher J. Hardy, and Lior Wolf "Correcting motion artifacts in MRI scans using a deep neural network with automatic motion timing detection", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 1159514 (15 February 2021); https://doi.org/10.1117/12.2580869

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