Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
463-01-007 ISMRM Abstract

Highly accelerated 3D water/fat LGE imaging with deep-learning motion estimation and motion corrected reconstruction

Primary: Cardiovascular - Myocardium
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
463-01-007 · Data-Driven Reconstruction: From Networks to Clinical Practice · Tuesday, 12 May, 8:20 AM–9:15 AM · Digital Posters Row D
Keywords: Motion Correction Late gadolinium enhancement Deep-learning-based image reconstruction 3D MRI
Accepted
Dongyue Si 1, Andrew Phair1,2, Alina Hua1, Simon J Littlewood1, Michael Crabb1, Haikun Qi3, Tevfik Ismail1, Claudia Prieto1,4,5, Rene M Botnar1,4,5,6,7
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
2Image X Institute, The University of Sydney, Sydney, Australia
3School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
4School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, Chile
5Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Santiago, Chile
6Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, Chile
7Institute for Advanced Study, Technical University of Munich, Garching, Germany
Presenting Author: Dongyue Si

Synopsis

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References

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2. Munoz C, Bustin A, Neji R, et al. Motion-corrected 3D whole-heart water-fat high-resolution late gadolinium enhancement cardiovascular magnetic resonance imaging. Journal of Cardiovascular Magnetic Resonance. 2020;22(1):53. doi:10.1186/s12968-020-00649-5 [doi]
3. Aggarwal HK, Mani MP, Jacob M. MoDL: Model-Based Deep Learning Architecture for Inverse Problems. IEEE Trans Med Imaging. 2019;38(2):394-405. doi:10.1109/TMI.2018.2865356 [doi]
4. El-Rewaidy H, Neisius U, Mancio J, et al. Deep complex convolutional network for fast reconstruction of 3D late gadolinium enhancement cardiac MRI. NMR Biomed. 2020;33(7):e4312. doi:https://doi.org/10.1002/nbm.4312 [doi]
5. Qi H, Hajhosseiny R, Cruz G, et al. End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA. Magn Reson Med. 2021;86(4):1983-1996. doi:https://doi.org/10.1002/mrm.28851 [doi]
6. Phair A, Fotaki A, Felsner L, et al. A motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease. Journal of Cardiovascular Magnetic Resonance. 2024;26(1):101039. doi:https://doi.org/10.1016/j.jocmr.2024.101039 [doi]
7. Henningsson M, Koken P, Stehning C, Razavi R, Prieto C, Botnar RM. Whole-heart coronary MR angiography with 2D self-navigated image reconstruction. Magn Reson Med. 2012;67(2):437-445. doi:10.1002/mrm.23027 [doi]
8. Prieto C, Doneva M, Usman M, et al. Highly efficient respiratory motion compensated free-breathing coronary mra using golden-step Cartesian acquisition. Journal of Magnetic Resonance Imaging. 2015;41(3):738-746. doi:10.1002/jmri.24602 [doi]
9. Qi H, Fuin N, Cruz G, et al. Non-Rigid Respiratory Motion Estimation of Whole-Heart Coronary MR Images Using Unsupervised Deep Learning. IEEE Trans Med Imaging. 2021;40(1):444-454. doi:10.1109/TMI.2020.3029205 [doi]
10. Dixon WT. Simple proton spectroscopic imaging. Radiology. 1984;153(1):189-194. doi:10.1148/radiology.153.1.6089263 [doi]

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http://echo.ismrm.org/p/ISMRM2026/463-01-007