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Updated: Mar 29, 2026

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
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Benchmarking Hybrid CNN-Transformer Versus Pure Transformer Architectures for Accelerated Hyperpolarized 129Xe MRI

Ramtin Babaeipour1, Matthew S Fox2,3,4, Grace Parraga1,3,4,5

  • 1School of Biomedical Engineering, Faculty of Engineering, The University of Western Ontario, London, Ontario, Canada.

Journal of Magnetic Resonance Imaging : JMRI
|March 27, 2026
PubMed
Summary

A novel hybrid transformer-CNN architecture, KTMR, significantly improves hyperpolarized 129Xe MRI reconstruction. This advancement addresses low signal-to-noise challenges, enhancing diagnostic accuracy for lung diseases.

Keywords:
COPDMRI reconstructionVision Transformersasthmadeep learninghyperpolarized 129Xe MRIlong‐COVIDlung imagingmedical imagingpulmonary imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Pulmonary Medicine

Background:

  • Hyperpolarized 129Xe MRI faces challenges like low signal-to-noise ratio and breath-hold limitations.
  • Existing solutions often rely on proprietary deep learning or image-domain methods.

Purpose of the Study:

  • To evaluate transformer and hybrid CNN-transformer architectures for hyperpolarized 129Xe MRI reconstruction.
  • Focus on dual-domain processing (k-space and image) to enhance image quality.

Main Methods:

  • Retrospective analysis of 205 participants (healthy, COPD, asthma, long-COVID).
  • Comparison of five architectures (KTMR, KIKI-net, ReconFormer, SwinMR, MR-IPT) at acceleration factors 3, 7, and 10.
  • Performance assessed using PSNR, SSIM, NMSE, and Ventilation Defect Percentage (VDP) agreement.

Main Results:

  • KTMR significantly outperformed other architectures at 10-fold acceleration (PSNR 36.4±2.8 dB, SSIM 0.88±0.12).
  • VDP measurements showed minimal bias across acceleration factors (1.94% to 2.69%).

Conclusions:

  • KTMR demonstrates superior performance for hyperpolarized 129Xe MRI reconstruction, especially at high acceleration factors.
  • This hybrid approach offers a promising solution for improving diagnostic capabilities in lung imaging.