Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
463-01-009 ISMRM Abstract

Learning Patient-Adaptive Undersampling Patterns for Cardiac MRI Using Nearest Neighbor Search

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - Subsampling: Parallel imaging, compressed sensing and low-rank modeling
463-01-009 · Data-Driven Reconstruction: From Networks to Clinical Practice · Tuesday, 12 May, 8:20 AM–9:15 AM · Digital Posters Row D
Keywords: Image Reconstruction Deep learning reconstruction Sampling pattern optimization Cardiac Cine MRI Accelerated MRI reconstruction
Accepted
Siddhant Gautam1, Angqi Li2, Jeffrey A Fessler 3,4,5, Nicole Seiberlich3, Saiprasad Ravishankar1,6
1Department of Computational Mathematics Science and Engineering, Michigan State Universtiy, East Lansing, United States of America
2Computational Mathematics Science and Engineering, Michigan State Universtiy, East Lansing, United States of America
3Department of Radiology, University of Michigan, Ann Arbor, United States of America
4Department of Biomedical Engineering, University of Michigan, Ann Arbor, United States of America
5Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, United States of America
6Department of Biomedical Engineering, Michigan State Universtiy, East Lansing, United States of America
Presenting Author: Jeffrey A Fessler

Synopsis

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References

1. Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med. 2007;58(6):1182–1195. doi:10.1002/mrm.21391 [doi]
2. Bahadir CD, Wang AQ, Dalca AV, Sabuncu MR. Deep-learning-based optimization of the under-sampling pattern in MRI. IEEE Trans Comput Imaging. 2020;6:1139–1152. doi:10.1109/TCI.2020.3037869 [doi]
3. Zibetti MVW, Herman GT, Regatte RR. Fast data-driven learning of parallel MRI sampling patterns for large-scale problems. Sci Rep. 2021;11(1):19312. doi:10.1038/s41598-021-98605-5 [doi]
4. Zibetti MVW, Knoll F, Regatte RR. Alternating learning approach for variational networks and undersampling pattern in parallel MRI applications. IEEE Trans Comput Imaging. 2022;8:449–461. doi:10.1109/TCI.2022.3148679 [doi]
5. Gautam S, Li A, Ravishankar S. Patient-adaptive and learned MRI data undersampling using neighborhood clustering. In: ICASSP 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2024:2081–2085. doi:10.1109/ICASSP48485.2024.10444381 [doi]
6. Gautam S, Li A, Seiberlich N, Fessler JA, Ravishankar S. Scan-adaptive MRI undersampling using neighbor-based optimization (SUNO). arXiv preprint arXiv:2501.09799, 2025.
7. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI. Magn Reson Med. 1999;42(5):952–962. doi:10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S [doi]
8. Qin C, Duan J, Hammernik K, Schlemper J, Küstner T, Botnar R, Prieto C, Price AN, Hajnal JV, Rueckert D. Complementary time-frequency domain networks for dynamic parallel MR image reconstruction. Magn Reson Med. 2021;86(6):3274–3291. doi:10.1002/mrm.28973 [doi]
9. 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]

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http://echo.ismrm.org/p/ISMRM2026/463-01-009