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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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Parallel computing of patch-based nonlocal operator and its application in compressed sensing MRI.

Qiyue Li1, Xiaobo Qu1, Yunsong Liu1

  • 1Departments of Communication Engineering and Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen 361005, China.

Computational and Mathematical Methods in Medicine
|June 26, 2014
PubMed
Summary
This summary is machine-generated.

Compressed sensing magnetic resonance imaging (CS-MRI) is slow due to iterative reconstruction. A new parallel architecture accelerates patch-based nonlocal operators (PANO) for faster CS-MRI reconstruction in seconds.

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Area of Science:

  • Medical Imaging
  • Computer Science
  • Signal Processing

Background:

  • Compressed sensing (CS) enhances magnetic resonance imaging (MRI) speed.
  • Iterative reconstruction in CS-MRI leads to long computation times.
  • Patch-based nonlocal operators (PANO) reduce CS-MRI reconstruction error using image self-similarity.

Purpose of the Study:

  • To accelerate the computationally intensive PANO method for CS-MRI.
  • To enable faster PANO-based CS-MRI reconstruction without compromising accuracy.

Main Methods:

  • A parallel architecture utilizing multicore processors was developed.
  • This architecture accelerates the similarity learning and 3D wavelet transform steps within PANO.
  • The method was evaluated through simulations for CS-MRI reconstruction.

Main Results:

  • The proposed parallel architecture significantly reduces PANO computation time.
  • Achieved acceleration factors closely match the number of CPU cores utilized.
  • PANO-based CS-MRI reconstruction is accomplished within seconds.

Conclusions:

  • A parallel multicore architecture effectively accelerates PANO for CS-MRI.
  • This advancement makes CS-MRI reconstruction significantly faster and more practical.
  • The approach holds promise for real-time or near-real-time medical imaging applications.