Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
466-03-011 ISMRM Abstract

DL-ZTE for structure-function lung imaging in healthy volunteers: Comparison to UTE and conventional ZTE

Primary: Body - Lung
Secondary: Acquisition & Reconstruction - Artifacts and Correction Strategies
466-03-011 · Pulse Sequences with a Focus on Quantitative Imaging · Tuesday, 12 May, 1:40 PM–2:35 PM · Digital Posters Row G
Keywords: Deep learning reconstruction Zero TE Lung MRI
Accepted
Amy V Simmons1, José de Arcos, Sagar Mandava2, Jim Wild1,3, Neil J Stewart 1,3
1POLARIS, Division of Clinical Medicine, School of Medicine & Population Health, The University of Sheffield, Sheffield, United Kingdom
2GE HealthCare (Atlanta, USA), United States of America
3INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
Presenting Author: Neil J Stewart

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References

1. Johnson, K.M., Fain, S.B., Schiebler, M.L. and Nagle, S. (2013), Optimized 3D ultrashort echo time pulmonary MRI. Magn. Reson. Med., 70: 1241-1250. https://doi.org/10.1002/mrm.24570 [doi]
2. Gibiino, F., Sacolick, L., Menini, A. et al. Free-breathing, zero-TE MR lung imaging. Magn Reson Mater Phy 28, 207–215 (2015). https://doi.org/10.1007/s10334-014-0459-y [doi]
3. Bae, K., Jeon, K.N., Hwang, M.J. et al. Comparison of lung imaging using three-dimensional ultrashort echo time and zero echo time sequences: preliminary study. Eur Radiol 29, 2253–2262 (2019). https://doi.org/10.1007/s00330-018-5889-x [doi]
4. Carl M, Wang J, Lo J, Chung JH, Du J, Ma Y. Optimized 3D UTE and ZTE MRI for high-resolution lung imaging: A comparative study. Magn Reson Med. 2025; 1-13. doi: 10.1002/mrm.70100 [doi]
5. Marshall, H., Voskrebenzev, A., Smith, L.J., Biancardi, A.M., Kern, A.L., Collier, G.J., Wielopolski, P.A., Ciet, P., Tiddens, H.A.W.M., Vogel-Claussen, J. and Wild, J.M. (2023), 129Xe and Free-Breathing 1H Ventilation MRI in Patients With Cystic Fibrosis: A Dual-Center Study. J Magn Reson Imaging, 57: 1908-1921. https://doi.org/10.1002/jmri.28470 [doi]
6. Lebel, R. M. Performance characterization of a novel deep learning-based MR image reconstruction pipeline. arXiv [eess.IV] (2020).
7. Astley, J.R., Biancardi, A.M., Hughes, P.J.C., Marshall, H., Collier, G.J., Chan, H.-F., Saunders, L.C., Smith, L.J., Brook, M.L., Thompson, R., Rowland-Jones, S., Skeoch, S., Bianchi, S.M., Hatton, M.Q., Rahman, N.M., Ho, L.-P., Brightling, C.E., Wain, L.V., Singapuri, A., Evans, R.A., Moss, A.J., McCann, G.P., Neubauer, S., Raman, B., C-MORE/PHOSP-COVID Collaborative Group, Wild, J.M. and Tahir, B.A. (2023), Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study. J Magn Reson Imaging, 58: 1030-1044. https://doi.org/10.1002/jmri.28643 [doi]

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http://echo.ismrm.org/p/ISMRM2026/466-03-011