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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
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Compressed sensing MRI: a review from signal processing perspective.

Jong Chul Ye1

  • 1Department of Bio and Brain Engineering, Korea Adv. Inst. of Science & Technology (KAIST), 291 Daehak-ro, Daejeon, Korea.

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|September 9, 2020
PubMed
Summary
This summary is machine-generated.

Compressed sensing (CS) accelerates Magnetic Resonance Imaging (MRI) by reconstructing images from undersampled data. This review covers CS principles and its evolution for faster MRI acquisition.

Keywords:
MRI; compressed sensing; k-space

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Biophysics

Background:

  • Magnetic Resonance Imaging (MRI) is limited by slow data acquisition, hindering high-resolution and dynamic imaging.
  • Fast MRI techniques are crucial for expanding clinical applications and improving patient experience.

Purpose of the Study:

  • To review the fundamental principles of Compressed Sensing (CS).
  • To explore the development and application of CS in accelerating Magnetic Resonance Imaging (MRI).

Main Methods:

  • Review of Compressed Sensing (CS) theory and its adaptation for MRI.
  • Analysis of k-space undersampling strategies and reconstruction algorithms.
  • Discussion of CS advancements for various MRI challenges.

Main Results:

  • Compressed Sensing (CS) enables accurate image reconstruction from sparsely sampled k-space data.
  • Recent FDA approvals signify the clinical maturity and efficacy of CS in MRI.
  • CS has become a pivotal technology for achieving accelerated MRI acquisition.

Conclusions:

  • Compressed Sensing (CS) is a transformative technology for fast MRI.
  • The evolution of CS addresses key limitations in MRI scan speed.
  • CS-based MRI is poised for broader clinical adoption and advanced applications.