Image registration guided,sparsity constrained reconstructions for dynamic MRI |
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Authors: | Jin Jin Feng LiuStuart Crozier |
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Institution: | School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD 4072, Australia |
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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. |
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Keywords: | Compressed sensing (CS) Dynamic magnetic resonance imaging (dMRI) Cardiac cine Cardiac perfusion Non-rigid image registration Free-form deformation (FFD) |
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