Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
360-02-005 ISMRM Abstract

Scan-Adaptive Deep Learning-Based Sampling with Pre-Optimized Mask Supervision

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - Subsampling: Parallel imaging, compressed sensing and low-rank modeling
360-02-005 · Machine Learning for Image Reconstruction · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row A
Keywords: Image Reconstruction Magnetic Resonance Imaging (MRI) Deep learning Undersampling Scan-Adaptive Sampling
Accepted
Aryan Dhar1, Siddhant Gautam2, Saiprasad Ravishankar3,4
1Michigan State University, East Lansing, United States of America
2Department of Computational Mathematics Science and Engineering, Michigan State University, East Lansing, United States of America
3Department of Computational Mathematics Science and Engineering, Michigan State Universtiy, East Lansing, United States of America
4Department of Biomedical Engineering, Michigan State Universtiy, East Lansing, United States of America
Presenting Author: Jeffrey A Fessler

Synopsis

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References

1. Pruessmann KP. Encoding and reconstruction in parallel MRI. NMR Biomed. 2006;19(3):288–299. doi:10.1002/nbm.1042 [doi]
2. 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]
3. Bahadir CD, Wang AQ, Dalca AV, Sabuncu MR. Deep-learning-based optimization of the undersampling pattern in MRI. IEEE Trans Comput Imaging. 2020;6:1139–1152. doi:10.1109/TCI.2020.3037869 [doi]
4. 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. 2024. doi:10.1109/TMI.2024.1234567 [doi]
5. Yin T, Wu Z, Sun H, Dalca AV, Yue Y, Bouman KL. End-to-end sequential sampling and reconstruction for MR imaging. Proc Mach Learn Health Conf. 2021.
6. Huang Z, Ravishankar S. Single-pass object-adaptive data undersampling and reconstruction for MRI. IEEE Trans Comput Imaging. 2022;8:333–345. doi:10.1109/TCI.2022.3145678 [doi]
7. Gautam S, Li A, Ravishankar S. Patient-adaptive and learned MRI data undersampling using neighborhood clustering. ICASSP 2024 – IEEE Int Conf Acoust Speech Signal Process. 2024:2081–2085.
8. 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.
9. Zbontar J, Knoll F, Sriram A, Murrell T, Huang Z, Muckley MJ, Defazio A, Stern R, Johnson P, Bruno M, et al. fastMRI: An open dataset and benchmarks for accelerated MRI. arXiv preprint arXiv:1811.08839, 2018.
10. Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly JM, Vasanawala SS, Lustig M. ESPIRiT—An eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA. Magn Reson Med. 2014;71(3):990–1001. doi:10.1002/mrm.24751 [doi]
11. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. MICCAI. 2015:234–241.
12. H.K. Aggarwal, M.P. Mani, and M. Jacob, “MoDL: Model-based deep learning architecture for inverse problems,” IEEE Trans. on Med. Img., vol. 38, no. 2, pp. 394–405, 2018.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/360-02-005