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Compressed Sensing and Beyond

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Protocols and Methodologies in Basic Science and Clinical Cardiac MRI

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Abstract

Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) have become well-established tools in basic cardiovascular research to non-invasively provide multi-parametric characterization of hearts in small animal models of human disease. Technical challenges and long scan times frequently preclude a comprehensive, multi-parametric investigation in the same animal, increasing noise, and ultimately the number of animals required to conduct a pre-clinical study. Dedicated acceleration techniques that do not compromise diagnostic accuracy are urgently needed to fully realize the potential of these powerful techniques. While hardware-based solutions (i.e., parallel imaging (PI) techniques) typically provide only two to threefold accelerations in the data acquisition speed, Compressed Sensing (CS) is an alternative approach, which potentially allows for higher scan time reductions. Importantly, it does not rely on specific hardware requirements for the data acquisition, and can in principle be applied on any (pre-) clinical MR optimisation system. CS utilises the fact that the majority of medical images are compressible (i.e., sparse) in some transform domain. The reconstruction uses a non-linear optimisation algorithm, exploiting both the undersampled, incoherent data acquisition and the sparsity model. This chapter explains the basics of CS in the context of MRI, reviews the role of CS in pre-clinical/clinical cardiac MR, and provides examples for the successful applications in cardiac phenotyping of mouse and rat hearts.

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Abbreviations

ASL:

Arterial spin labelling

BLOSM:

Block low-rank sparsity with motion-guidance

CS:

Compressed sensing

CSI:

Chemical shift imaging

FISTA:

Fast iterative shrinkage/thresholding algorithm

MPR:

Myocardial perfusion reserve

MRS:

Magnetic resonance spectroscopy

PI:

Parallel imaging

POCS:

Projection onto convex sets

RF:

Radio frequency

TPSF:

Transform point spread function

TV:

Total variation

TWIST:

Two step iterative shrinkage/thresholding algorithm

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Acknowledgements

TW acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, KO 2938/4-1) and from Siemens Healthcare GmbH. JES is a Senior British Heart Foundation (BHF) Basic Science Research Fellow (FS/11/50/29038), and acknowledges funding from the BHF, the Oxford Centre of Research Excellence, and the Oxbridge Centre of Regenerative Medicine.

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Authors and Affiliations

  1. Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany

    Tobias Wech

  2. British Heart Foundation Magnetic Resonance Imaging Unit (BMRU), Division of Cardiovascular Medicine, Wellcome Trust of Human Genetics of Oxford, Oxford, UK

    Jürgen Ernst Schneider

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  1. Tobias Wech
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Correspondence to Tobias Wech .

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  1. Department of Cardiovascular Medicine, BMRU, Oxford, United Kingdom

    Christakis Constantinides

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Wech, T., Schneider, J.E. (2018). Compressed Sensing and Beyond. In: Constantinides, C. (eds) Protocols and Methodologies in Basic Science and Clinical Cardiac MRI. Springer, Cham. https://doi.org/10.1007/978-3-319-53001-7_9

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