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Compressed sensing MRI: a review.

Sairam Geethanath1, Rashmi Reddy2, Amaresha Shridhar Konar3

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Compressed sensing (CS) accelerates Magnetic Resonance Imaging (MRI) by reconstructing data from fewer measurements. This review details CS requirements for optimal MRI, focusing on sparsity, undersampling, and reconstruction for faster imaging.

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Applied Mathematics

Background:

  • Compressed sensing (CS) is a signal processing technique enabling data reconstruction from undersampled measurements.
  • CS has been applied to Magnetic Resonance Imaging (MRI) to significantly accelerate image acquisition times.
  • Optimal application of CS in MRI requires specific conditions: transform sparsity, pseudo-random undersampling, and nonlinear reconstruction.

Purpose of the Study:

  • To review the fundamental principles of compressed sensing and its application in MRI.
  • To outline the essential requirements for qualifying MRI as an optimal application for CS.
  • To discuss various aspects of CS in MRI, including acquisition and reconstruction techniques, parallel imaging integration, and sampling mask design.

Main Methods:

  • Review of existing literature on compressed sensing applied to MRI.
  • Analysis of the theoretical underpinnings of sparsity and compression in MR signal acquisition.
  • Discussion of diverse MRI methods where CS has been successfully implemented.

Main Results:

  • CS enables acquiring only essential signal coefficients, leveraging a priori knowledge of MR image sparsity.
  • Successful application of CS in MRI leads to improved imaging speed across various techniques.
  • Clinical evaluations demonstrate the feasibility and benefits of CS applications in recent MRI studies.

Conclusions:

  • Compressed sensing is a powerful framework for accelerating MRI acquisition.
  • Understanding and implementing sparsity, undersampling, and nonlinear reconstruction are key for effective CS in MRI.
  • The review provides insights into CS applications, clinical evaluations, and available open-source software for CS implementations in MRI.