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Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling
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Data-driven self-calibration and reconstruction for non-cartesian wave-encoded single-shot fast spin echo using deep

Feiyu Chen1, Joseph Y Cheng2, Valentina Taviani3

  • 1Department of Electrical Engineering, Stanford University, Stanford, California, USA.

Journal of Magnetic Resonance Imaging : JMRI
|July 20, 2019
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Summary
This summary is machine-generated.

This study introduces a faster, data-driven method for wave-encoded single-shot fast spin echo imaging (SSFSE) reconstruction. The new approach significantly reduces computation time and perceived noise, improving image quality for abdominal scans.

Keywords:
data-drivendeep learningparallel imaging and compressed sensingsingle-shot fast spin echowave encoding

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Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging
  • Artificial Intelligence in Medicine

Background:

  • Current self-calibration and reconstruction for wave-encoded single-shot fast spin echo imaging (SSFSE) is computationally intensive.
  • High accuracy requirements exacerbate the computational time burden.

Purpose of the Study:

  • To develop and assess the clinical feasibility of a data-driven approach for self-calibration and reconstruction in wave-encoded SSFSE imaging.
  • To reduce computation time and enhance image quality.

Main Methods:

  • Developed a wave-encoded variable-density SSFSE sequence for 3.0T abdominal scans, allowing 3.5x acceleration.
  • Utilized deep neural networks for data-driven calibration of the wave-encoding point-spread function (PSF).
  • Employed another set of neural networks for data-driven reconstruction based on the calibrated PSF, trained on over 15,000 images.

Main Results:

  • Achieved an average 2.1-fold speedup in computation time compared to conventional methods.
  • Significantly reduced perceived noise levels (mean score 0.82, P < 0.0001).
  • Image quality was evaluated by three radiologists, comparing the data-driven approach against iterative methods.

Conclusions:

  • The proposed data-driven self-calibration and reconstruction method offers a faster and robust solution for clinical abdominal SSFSE imaging.
  • Achieved approximately twice the computation speed with reduced perceived noise.
  • Demonstrated clinical feasibility for improved MR imaging workflows.