Deep Learning Reconstruction for <scp> <sup>129</sup> Xe </scp> Diffusion‐Weighted <scp>MRI</scp> Enables Use of Natural Abundant Xenon and Improved Image Acceleration
DL-based CS, denoising and de-ringing significantly improves SNR and image sharpness in 3D <sup>129</sup>Xe diffusion-weighted MRI while exhibiting a slight bias in ADC and Lm<sub>D</sub>. This approach enables the use of natural-abundance xenon and higher acceleration factors, offering substantial cost reduction and improved clinical feasibility for hyperpolarized <sup>129</sup>Xe lung morphometry.
Authors
- Jim M. Wild (ORCID: https://orcid.org/0000-0002-7246-8660)
- Rajagopalan Sundaresan (ORCID: https://orcid.org/0009-0003-1176-8373)
- Ramesh Venkatesan
- Guilhem Collier (ORCID: https://orcid.org/0000-0002-1874-4775)
- Neil J. Stewart (ORCID: https://orcid.org/0000-0001-8358-394X)
- Sudhanya Chatterjee (ORCID: https://orcid.org/0000-0002-6767-2958)
- Jan Wolber
Institutions
- General Electric (Spain) (ES)
- Insigneo (GB)
- University of Sheffield (GB)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1002/mrm.70194
- Citations
- 1
- Primary Topic
- Atomic and Subatomic Physics Research
- Type
- article
- Field-Weighted Citation Impact
- 1.25
Funders
- National Institute for Health and Care Research
- Department of Health and Social Care
- Medical Research Council
- Engineering and Physical Sciences Research Council
- NIHR Sheffield Biomedical Research Centre