Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
503-03-003 ISMRM Abstract

AdaSamp: Towards simple, subject-specific Adaptive Sampling for 3D Accelerated MRI

Primary: Acquisition & Reconstruction - Subsampling: Parallel imaging, compressed sensing and low-rank modeling
Secondary: Acquisition & Reconstruction - AI methods
503-03-003 · AI: Emerging Techniques and Clinical Applications · Wednesday, 13 May, 4:00 PM–5:50 PM · Auditorium 1
Keywords: AI/ML Image Reconstruction Sampling pattern optimization Accelerated Acquisition Accelerated MRI reconstruction Variable-density sampling
Accepted
Jaehyeok Bae 1, Zachary Shah1, Cagan Alkan1, Shreyas Vasanawala2,3, John Pauly1,2, Kawin Setsompop1,3,4
1Electrical Engineering, Stanford University, Stanford, United States of America
2Stanford University, Stanford, United States of America
3Department of Radiology, Stanford University, Stanford, United States of America
4Stanford Medicine, Stanford, United States of America
Presenting Author: Jaehyeok Bae

Synopsis

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References

1. Wang G, Luo T, Nielsen J-F, Noll DC, Fessler JA. B-spline parameterized joint optimization of reconstruction and k-space trajectories (BJORK) for accelerated 2D MRI. Arch Neurol. 2004;61(7):1025-1029. IEEE Trans Med Imaging. 2022;41(9):2318-2330. doi:10.1109/TMI.2022.3161875. PMID: 35320096. [doi] [pmid]
2. Aggarwal HK, Jacob M. J-MoDL: Joint model-based deep learning for optimized sampling and reconstruction. IEEE J Sel Top Signal Process. 2020;14(6):1151-1162. doi:10.1109/JSTSP.2020.3004094. PMID: 33613806. [doi] [pmid]
3. Peng W, Feng L, Zhao G, Liu F. Learning optimal k-space acquisition and reconstruction using physics-informed neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022:20794-20803. doi:10.1109/CVPR52688.2022.02013. [doi]
4. Bahadir CD, Dalca AV, Sabuncu MR. Learning-based optimization of the under-sampling pattern in MRI. In: Information Processing in Medical Imaging (IPMI 2019). Cham, Switzerland: Springer; 2019:780-792. doi:10.1007/978-3-030-20351-1_61. [doi]
5. Alkan C, Mardani M, Liao C, Li Z, Vasanawala SS, Pauly JM. AutoSamp: Autoencoding k-space sampling via variational information maximization for 3D MRI. IEEE Trans Med Imaging. 2025;44(1):270-283. doi:10.1109/TMI.2024.3443292. PMID: 39146168. [doi] [pmid]
6. Bae J, Alkan C, Vasanawala S, Pauly JM, Setsompop K. QuickSamp: Towards simple, real-time-optimized sampling patterns for 3D accelerated MRI. Proc Intl Soc Magn Reson Med. 2025;1367.
7. Breuer FA, Blaimer M, Mueller MF, et al. Controlled aliasing in volumetric parallel imaging (2D CAIPIRINHA). Magn Reson Med. 2006;55(3):549-556. doi:10.1002/mrm.20787. PMID: 16538635. [doi] [pmid]
8. Lustig M, Pauly JM. SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magn Reson Med. 2010;64(2):457-471. doi:10.1002/mrm.22428. PMID: 20665790. [doi] [pmid]
9. Ong F, Amin S, Vasanawala S, Lustig M. mridata.org: An open archive for sharing MRI raw data. Proc ISMRM-ESMRMB Joint Annual Meeting. 2018;3425.
10. Stanford Longitudinally Accelerated MRI (SLAM) Dataset. Stanford Digital Repository; 2025. doi:10.25740/rq296rb2765. [doi]
11. Urman Y, Shah Z, Kumar A, Soares BP, Setsompop K. Accelerating MRI with longitudinally-informed latent posterior sampling. arXiv. 2025;arXiv:2407.00537. doi:10.48550/arXiv.2407.00537. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/503-03-003