Image registration guided, sparsity constrained reconstructions for dynamic MRI

Jin, Jin, Liu, Feng and Crozier, Stuart (2014) Image registration guided, sparsity constrained reconstructions for dynamic MRI. Magnetic Resonance Imaging, 1-15. doi:10.1016/j.mri.2014.08.006

Author Jin, Jin
Liu, Feng
Crozier, Stuart
Title Image registration guided, sparsity constrained reconstructions for dynamic MRI
Journal name Magnetic Resonance Imaging   Check publisher's open access policy
ISSN 0730-725X
Publication date 2014-08-14
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.mri.2014.08.006
Open Access Status Not Open Access
Start page 1
End page 15
Total pages 15
Place of publication Philadelphia, PA, United States
Publisher Elsevier
Language eng
Formatted abstract
It is generally a challenging task to reconstruct dynamic magnetic resonance (MR) images with high spatial and high temporal resolutions, especially with highly incomplete k-space sampling. In this work, a novel method that combines a non-rigid image registration technique with sparsity-constrained image reconstruction is introduced. Employing a multi-resolution free-form deformation technique with B-spline interpolations, the non-rigid image registration accurately models the complex deformations of the physiological dynamics, and provides artifact-suppressed high spatial-resolution predictions. Based on these prediction images, the sparsity-constrained data fidelity-enforced image reconstruction further improves the reconstruction accuracy. When compared with the k-t FOCUSS with motion estimation/motion compensation (MEMC) technique on volunteer scans, the proposed method consistently outperforms in both the spatial and the temporal accuracy with variously accelerated k-space sampling. High fidelity reconstructions for dynamic systolic phases with reduction factor of 10 and cardiac perfusion series with reduction factor of 3 are presented.
Keyword Compressed sensing (CS)
Dynamic magnetic resonance imaging (dMRI)
Cardiac cine
Cardiac perfusion
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 4 times in Scopus Article | Citations
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Created: Tue, 19 Aug 2014, 01:38:53 EST by Mr Jin Jin on behalf of School of Information Technol and Elec Engineering