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Introduction to Compressed Sensing Magnetic Resonance Imaging

  • Bhabesh DekaEmail author
  • Sumit Datta
Chapter
Part of the Springer Series on Bio- and Neurosystems book series (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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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringTezpur UniversityTezpurIndia

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