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Robust partial Fourier reconstruction for diffusion-weighted imaging using a recurrent convolutional neural network.

Fasil Gadjimuradov1,2, Thomas Benkert2, Marcel Dominik Nickel2

  • 1Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.

Magnetic Resonance in Medicine
|November 29, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for robust partial Fourier (PF) reconstruction in diffusion-weighted (DW) imaging, improving image quality and reducing artifacts in challenging anatomies.

Keywords:
deep learningdiffusion-weighted imaginglearning-based reconstructionliver imagingpartial Fourier imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Diffusion-Weighted MRI

Background:

  • Partial Fourier (PF) imaging accelerates data acquisition but can introduce artifacts, especially in diffusion-weighted (DW) images with phase variations.
  • Robust reconstruction algorithms are needed to overcome limitations of conventional PF techniques in complex anatomical regions.

Purpose of the Study:

  • To develop a robust partial Fourier (PF) reconstruction algorithm for diffusion-weighted (DW) images, specifically addressing challenges posed by non-smooth phase variations.
  • To enhance the quality and reliability of accelerated MRI scans using advanced computational methods.

Main Methods:

  • An unrolled proximal splitting algorithm was adapted into a neural network architecture, combining data consistency and recurrent convolutional regularization.
  • Joint reconstruction of multiple slice repetitions, incorporating permutation-equivariance, was employed to leverage data correlations.
  • The algorithm was trained on DW liver data and validated on diverse retrospective and prospective datasets across different anatomies and resolutions.

Main Results:

  • The proposed method significantly outperformed conventional PF techniques in quantitative and perceptual image quality on retrospectively subsampled data.
  • Joint reconstruction of repetitions and recurrent network unrolling proved beneficial for reconstruction quality.
  • On prospectively acquired data, the method enabled higher signal-to-noise ratio DW imaging without increased artifacts or resolution loss, and demonstrated generalizability to unseen brain data.

Conclusions:

  • Robust PF reconstruction of DW data is achievable even with high acceleration factors and in anatomies with phase variations.
  • The learned recurrent convolutions avoid reliance on phase smoothness priors, mitigating artifacts common in conventional PF methods.
  • This approach offers a pathway to improved accelerated MRI acquisition protocols, enhancing diagnostic capabilities.