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Improved reconstruction for highly accelerated propeller diffusion 1.5 T clinical MRI.

Uten Yarach1, Itthi Chatnuntawech2, Kawin Setsompop3

  • 1Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand. uten178@yahoo.co.th.

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Summary

This study introduces a faster, high-quality method for Cholesteatoma diagnosis using Convolutional Neural Networks (CNNs) in diffusion MRI. The new approach significantly improves image quality and reduces scan time for propeller FSE-dMRI.

Keywords:
CholesteatomaDiffusion MRILocally Low RankNon-EPIResidual U-Net

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Diffusion MRI

Background:

  • Propeller fast-spin-echo diffusion magnetic resonance imaging (FSE-dMRI) is crucial for diagnosing Cholesteatoma.
  • Clinical 1.5 T MRI often yields low signal-to-noise ratio (SNR) in FSE-dMRI, necessitating longer scan times with increased signal averaging (number of excitations, NEX).

Purpose of the Study:

  • To enhance SNR and accelerate PROPELLER FSE-dMRI on a 1.5 T clinical scanner using Locally Low Rank (LLR) reconstruction and Convolutional Neural Networks (CNNs).
  • To reduce scan time while maintaining or improving diagnostic image quality for Cholesteatoma detection.

Main Methods:

  • A Residual U-Net (RU-Net) architecture was developed and trained using 1-NEX FSE-dMRI data as input.
  • The network was trained to predict 2-NEX images reconstructed with Locally Low Rank (LLR) constraints.
  • Brain scans from healthy volunteers and patients with Cholesteatoma were utilized for training and validation.

Main Results:

  • Offline LLR reconstruction improved image quality by suppressing noise and revealing small structures compared to online reconstruction.
  • RU-Net further enhanced image quality, achieving a 18.87% increase in PSNR, 2.11% in SSIM, and a 53.84% reduction in NRMSE compared to LLR.
  • RU-Net demonstrated a significant speed improvement, reconstructing images approximately 1500 times faster than LLR (0.03s vs. 47.59s per slice).

Conclusions:

  • LLR reconstruction effectively boosts SNR in propeller FSE-dMRI.
  • RU-Net significantly improves the performance of propeller FSE-dMRI in terms of PSNR, SSIM, and NRMSE.
  • The RU-Net method enables a 2x reduction in scan time by utilizing 1-NEX data and offers a substantial speed advantage over LLR reconstruction.