Compressed sensing MR image reconstruction using a motion-compensated reference

Magnetic Resonance Imaging
Huiqian Du, Fan Lam

Abstract

Compressed sensing (CS)-based methods have been proposed for image reconstruction from undersampled magnetic resonance data. Recently, CS-based schemes using reference images have also been proposed to further reduce the sampling requirement. In this study, we propose a new reference-constrained CS reconstruction method that accounts for the misalignment between the reference and the target image to be reconstructed. The proposed method uses a new image model that represents the target image as a linear combination of a motion-dependent reference image and a sparse difference image. We then use an efficient iterative algorithm to jointly estimate the motion parameters and the difference image from sparsely sampled data. Simulation results from a numerical phantom data set and an in vivo data set show that the proposed method can accurately compensate the motion effects between the reference and the target images and improve reconstruction quality. The proposed method should prove useful for several applications such as interventional imaging, longitudinal imaging studies and dynamic contrast-enhanced imaging.

References

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Citations

Nov 6, 2014·Computational and Mathematical Methods in Medicine·Di ZhaoWenbo Mei
Mar 13, 2016·Magnetic Resonance Imaging·Xin MiaoKrishna S Nayak
Jan 24, 2015·Magnetic Resonance Imaging·Yu HanLiping Fang
Oct 27, 2016·Medical Physics·Lior WeizmanDafna Ben Bashat
Dec 15, 2018·Medical Physics·Jiansen LiJun Zhao
Feb 9, 2021·Computational and Mathematical Methods in Medicine·Di ZhaoJincun Zheng

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