Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding

Abstract

Multishot Magnetic Resonance Imaging (MRI) is a promising imaging modality that can produce a high-resolution image with relatively less data acquisition time. The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis. Numerous efforts have been made to address this issue; however, all of these proposals are limited in terms of how much motion they can correct and the required computational time. In this paper, we propose a novel generative networks based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. The proposed framework first employs CG-SENSE reconstruction to produce the motion-corrupted image and then a generative adversarial network (GAN) is used to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establishes that the proposed method significantly robust and outperforms state-of-the-art motion correction techniques and also reduces severalfold of computational times.

This paper has been published in Scientific Reports Journal

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Cited by in corpus (4)

• Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review
• Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis
• Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning
• Highly efficient non-rigid registration in k-space with application to cardiac Magnetic Resonance Imaging