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Acquisition time reduction of diffusion-weighted liver imaging using deep learning image reconstruction.

Saif Afat1, Judith Herrmann1, Haidara Almansour1

  • 1Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany.

Diagnostic and Interventional Imaging
|February 14, 2023
PubMed
Summary

Deep learning accelerated diffusion-weighted imaging (DWIDL) in 1.5-T liver MRI significantly reduces acquisition time and noise without compromising image quality or diagnostic confidence.

Keywords:
Deep learningDiffusion-weighted imagingImage reconstructionLiverMagnetic resonance imagingSignal-to-noise ratio

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Liver MRI utilizes diffusion-weighted imaging (DWI) for diagnosis.
  • Standard DWI acquisition times can be lengthy, potentially impacting patient comfort and workflow.
  • Deep learning (DL) offers potential for accelerating MRI sequences.

Purpose of the Study:

  • To evaluate the impact of deep learning accelerated DWI (DWIDL) on 1.5-T liver MRI.
  • Assess changes in image quality, sharpness, and diagnostic confidence.
  • Determine feasibility of reduced acquisition time with DL reconstruction.

Main Methods:

  • Retrospective analysis of 100 patients undergoing 1.5-T liver MRI with standard DWI (DWIStd) and DL-accelerated DWI (DWIDL).
  • DWIDL simulated a 1 min 28 s acquisition, compared to 2 min 31 s for DWIStd.
  • Radiologists assessed image quality, artifacts, sharpness, and diagnostic confidence using a Likert scale; quantitative noise and SNR analysis performed.

Main Results:

  • DWIDL demonstrated significantly lower noise levels in the spleen, liver, and erector spinae muscles (P < 0.001).
  • Superior signal-to-noise ratio (SNR) for DWIDL was observed in multiple tissues at b=50 s/mm² and for ADC maps (P < 0.001).
  • No significant differences in artifacts or overall image quality were found between DWIDL and DWIStd (P > 0.05).

Conclusions:

  • Deep learning image reconstruction is feasible for 1.5-T liver DWI.
  • DWIDL significantly reduces acquisition time and noise.
  • DL reconstruction maintains or improves image quality and diagnostic confidence without compromising results.