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Single-shot spiral diffusion-weighted imaging at 7T using expanded encoding with compressed sensing.

Gabriel Varela-Mattatall1,2, Paul I Dubovan1,2, Tales Santini1,2

  • 1Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, Western University, London, Ontario, Canada.

Magnetic Resonance in Medicine
|April 10, 2023
PubMed
Summary
This summary is machine-generated.

Compressed sensing combined with expanded encoding significantly enhances single-shot spiral diffusion MRI quality. This approach improves spatial resolution and signal-to-noise ratio (SNR) for better imaging at 7 Tesla.

Keywords:
compressed sensingexpanded encoding modelfield monitoringhigher-order reconstructionnon-Cartesianspiral

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Medical Imaging Physics
  • Diffusion Weighted Imaging (DWI)

Background:

  • Current MRI reconstruction methods may be suboptimal for low signal-to-noise ratio (SNR) imaging like diffusion MRI.
  • Expanded encoding models account for spatially and time-varying field perturbations during reconstruction.
  • Compressed sensing (CS) offers a regularization technique that can improve image reconstruction.

Purpose of the Study:

  • To investigate the benefits of combining compressed sensing (CS) with an expanded encoding model for single-shot spiral diffusion MRI (DWI) at 7 Tesla.
  • To evaluate the impact on the trade-off between spatial resolution, readout time, and SNR.
  • To assess improvements in low-SNR and high-resolution imaging scenarios.

Main Methods:

  • In vivo accelerated single-shot spiral MRI data were acquired at 7T with varying acceleration factors (R=2x-6x) and spatial resolutions (1.1-1.5 mm).
  • Reconstructions were performed using an open-source GPU-enabled toolbox (MatMRI), incorporating the expanded encoding model with and without CS.
  • Simulations were used to quantitatively validate the impact of CS regularization against a known ground truth.

Main Results:

  • In vivo reconstructions demonstrated improved image quality and retention of fine details when CS was applied.
  • Simulations confirmed that the combined expanded encoding model and CS enhance reconstruction accuracy, reducing mean-squared error across tested acceleration factors.
  • The method showed improved performance in low-SNR conditions typical of diffusion MRI.

Conclusions:

  • The expanded encoding model and CS regularization are complementary techniques for single-shot spiral diffusion MRI.
  • This combined approach enables simultaneous improvements in spatial resolution and acceleration (higher R).
  • The findings suggest a pathway for more efficient and higher-quality diffusion MRI acquisition at 7T.