Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
369-05-012 ISMRM Abstract

Deep Learning Reconstruction for 129Xe Diffusion-Weighted MRI Enables Shorter Scan Times and Use of Natural Abundant Xenon

Primary: Contrast Mechanisms - Hyperpolarized MR (Gas)
Secondary: Body - Chest/Thorax
369-05-012 · Hyperpolarized Gases · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row J
Keywords: Hyperpolarized 129Xe MRI Denoising Lung MRI Functional lung imaging Xenon-129
Accepted
Rajagopalan Sundaresan 1,2, Guilhem J Collier1,3, Neil J Stewart1,3, SUDHANYA Chatterjee2, Ramesh Venkatesan2, Jan Wolber4, Jim Wild1,3
1POLARIS, Division of Clinical Medicine, School of Medicine & Population Health, The University of Sheffield, Sheffield, United Kingdom
2GE HealthCare, Bengaluru, India
3INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
4GE Healthcare (UK), United Kingdom
Presenting Author: Rajagopalan Sundaresan

Synopsis

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References

1. Chan HF, Stewart NJ, Norquay G, Collier GJ, Wild JM. 3D diffusion-weighted 129 Xe MRI for whole lung morphometry. Magn Reson Med. 2018;79(6):2986-2995. doi:10.1002/mrm.26960 [doi]
2. Chan HF, Stewart NJ, Parra-Robles J, Collier GJ, Wild JM. Whole lung morphometry with 3D multiple b-value hyperpolarized gas MRI and compressed sensing. Magn Reson Med. 2017;77(5):1916-1925. doi:10.1002/mrm.26279 [doi]
3. Kaushik SS, Cleveland ZI, Cofer GP, et al. Diffusion-weighted hyperpolarized 129Xe MRI in healthy volunteers and subjects with chronic obstructive pulmonary disease. Magn Reson Med. 2011;65(4):1154-1165. doi:10.1002/mrm.22697 [doi]
4. Sukstanskii AL, Yablonskiy DA. Lung morphometry with hyperpolarized 129Xe: theoretical background. Magn Reson Med. 2012;67(3):856-866. doi:10.1002/mrm.23056 [doi]
5. Stewart NJ, Norquay G, Griffiths PD, Wild JM. Feasibility of human lung ventilation imaging using highly polarized naturally abundant xenon and optimized three-dimensional steady-state free precession. Magn Reson Med. 2015;74(2):346-352. doi:10.1002/mrm.25732 [doi]
6. Collier GJ, Hughes PJC, Horn FC, et al. Single breath-held acquisition of coregistered 3D 129 Xe lung ventilation and anatomical proton images of the human lung with compressed sensing. Magn Reson Med. 2019;82(1):342-347. doi:10.1002/mrm.27713 [doi]
7. Stewart NJ, de Arcos J, Biancardi AM, et al. Improving Xenon-129 lung ventilation image SNR with deep-learning based image reconstruction. Magn Reson Med. 2024;92(6):2546-2559. doi:10.1002/mrm.30250 [doi]
8. Marshall H, Wild JM, Smith LJ, et al. Functional imaging in asthma and COPD: design of the NOVELTY ADPro substudy. ERJ Open Res. 2023;9(2):00344-2022. Published 2023 Apr 3. doi:10.1183/23120541.00344-2022 [doi]
9. J. Schlemper, J. Caballero, J. V. Hajnal, A. N. Price and D. Rueckert, "A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction," in IEEE Transactions on Medical Imaging, vol. 37, no. 2, pp. 491-503, Feb. 2018, doi: 10.1109/TMI.2017.2760978. [doi]
10. Lebel, R. Marc. "Performance characterization of a novel deep learning-based MR image reconstruction pipeline." arXiv preprint arXiv:2008.06559 (2020). https://doi.org/10.48550/arXiv.2008.06559 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/369-05-012