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Compressed sensing in dynamic MRI.

Urs Gamper1, Peter Boesiger, Sebastian Kozerke

  • 1Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology, Gloriastrasse 35, Zurich, Switzerland.

Magnetic Resonance in Medicine
|January 30, 2008
PubMed
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Compressed sensing (CS) shows promise for accelerating dynamic MRI by leveraging data sparsity. This technique offers improved temporal fidelity over k-t BLAST when sufficient sparsity and signal-to-noise ratio are present.

Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Image Reconstruction
  • Signal Processing

Background:

  • Compressed sensing (CS) requires data sparsity, a condition often unmet in standard MRI.
  • Dynamic MRI presents opportunities for sparsity through temporal Fourier transformation, especially when parts of the field-of-view (FOV) change rapidly.

Purpose of the Study:

  • To assess the feasibility of the compressed sensing (CS) framework for accelerating dynamic MRI acquisition.
  • To compare CS reconstruction performance against established methods like k-t BLAST.

Main Methods:

  • Utilized simulated datasets to evaluate CS reconstruction under varying reduction factors, noise, and sparsity levels.
  • Applied CS to in vivo cardiac cine and carotid artery velocity data.
  • Compared CS reconstructions with k-t broad-use linear acquisition speed-up technique (k-t BLAST).

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Main Results:

  • Demonstrated that CS can be effectively applied to dynamic MRI by exploiting temporal sparsity.
  • Simulations showed CS performance varied with reduction factors, noise, and sparsity.
  • CS reconstructions yielded improved temporal fidelity compared to k-t BLAST for tested datasets.

Conclusions:

  • Compressed sensing (CS) is a feasible technique for accelerated dynamic MRI, particularly when data sparsity is achievable.
  • Sufficient data sparsity and signal-to-noise ratio (SNR) are crucial for CS to outperform conventional methods like k-t BLAST.