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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Signal-to-noise ratio improvement in postmortem MRI using deep learning reconstruction (SwiftMR).

Hideki Hyodoh1, Shogo Shimbashi1, Rikiya Maruyama2

  • 1Department of Forensic Medicine, University of Fukui School of Medical Sciences, Japan.

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|April 22, 2026
PubMed
Summary
This summary is machine-generated.

Deep learning software SwiftMR significantly improved signal-to-noise ratio and image quality in low-field postmortem MRI scans. This advancement enhances postmortem MRI utility without extending examination times for forensic investigations.

Keywords:
Deep learning reconstructionForensic radiologyImage denoisingLow-field MRIPostmortem MRISignal-to-noise ratio (SNR)SwiftMR

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Area of Science:

  • Medical Imaging
  • Forensic Science
  • Artificial Intelligence in Medicine

Background:

  • Postmortem computed tomography (PMCT) is standard in death investigations.
  • Postmortem magnetic resonance imaging (PMMRI) use is limited by scan time and image quality requirements.
  • Deep learning software SwiftMR enhances MRI quality by denoising and optimizing k-space.

Purpose of the Study:

  • To evaluate SwiftMR's impact on signal-to-noise ratio (SNR) and image quality in low-field postmortem head MRI.
  • To determine if SwiftMR can improve PMMRI without increasing acquisition time.

Main Methods:

  • Thirty-three postmortem head MRI cases were analyzed using a 0.3T scanner.
  • T1-, T2-, and T2*-weighted images were acquired within 15 minutes.
  • Images were processed with SwiftMR and assessed for SNR and visual quality by blinded observers.

Main Results:

  • SwiftMR significantly increased SNR across all MRI sequences (p < 0.001).
  • Visual image quality scores were markedly higher after SwiftMR processing (7.5 ± 1.0 vs. 3.2 ± 0.8; p < 0.001).
  • No significant change in mean signal intensity, except for a slight decrease in T2WI due to bias correction.

Conclusions:

  • Deep learning-based SwiftMR effectively enhances image quality and SNR in low-field PMMRI.
  • SwiftMR offers a practical solution to improve PMMRI without extending scan times.
  • This technology may broaden the use of PMMRI as a complementary tool in forensic investigations.