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Introduction to Compressed Sensing Magnetic Resonance Imaging

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Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms

Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 9))

Abstract

Magnetic resonance imaging (MRI) is a widely used medical imaging tool where data acquisition is performed in the k-space, i.e., the Fourier transform domain. However, it has a fundamental limitation of being slow or having a long data acquisition time. Due to this, MRI is restricted in some clinical applications. Compressed sensing in MRI demonstrates that it is possible to reconstruct good quality MR images from a fewer k-space measurements. In this regard, convex optimization based \(\ell _1\)-norm minimization techniques are able to reconstruct MR images from undersampled k-space measurements with some computational overheads compared to the conventional MRI where inverse Fourier transform is sufficient to get images from the fully acquired k-space. A few practical implementations of compressed sensing in clinical MRI demonstrate that they are able to significantly reduce the imaging time of traditional MRI. This is a very significant development in the field of medical imaging as it would improve both the patient care and the healthcare economy.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Tezpur University, Tezpur, Assam, India

    Bhabesh Deka & Sumit Datta

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  1. Bhabesh Deka
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  2. Sumit Datta
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Correspondence to Bhabesh Deka .

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Deka, B., Datta, S. (2019). Introduction to Compressed Sensing Magnetic Resonance Imaging. In: Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms. Springer Series on Bio- and Neurosystems, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-13-3597-6_1

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  • DOI: https://doi.org/10.1007/978-981-13-3597-6_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3596-9

  • Online ISBN: 978-981-13-3597-6

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