Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
568-06-009 ISMRM Abstract

Wavelet-Regularized Subspace Reconstruction for Highly Accelerated Multi-Echo ASL MRI

Primary: Acquisition & Reconstruction - Subsampling: Parallel imaging, compressed sensing and low-rank modeling
Secondary: Acquisition & Reconstruction - Image Reconstruction: Non-AI
568-06-009 · Image Reconstruction II · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row I
Keywords: Compressed Sensing MRI Subspace Modelling Arterial Spin Labeling (ASL) Multi-Echo ASL Wavelet Regularization
Accepted
Ershad Hassanpour Golagani1,2, Yiran Li3, Bo Li3,4, Xiao Liang3,4, Yulin Chang5, Manuel Taso5, John Detre6,7,8, Min Wu2, Ze Wang 3,4
1Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of maryland Baltimore, Baltimore, United States of America
2Department of Electrical and Computer Engineering, University of Maryland, College Park, United States of America
3Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, United States of America
4Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, United States of America
5Siemens Medical Solutions USA, Inc., Malvern, United States of America
6University of Pennsylvania, Philadelphia, United States of America
7Department of Neurology, University of Pennsylvania, Philadelphia, United States of America
8Department of Radiology, University of Pennsylvania, Philadelphia, United States of America
Presenting Author: Ze Wang

Synopsis

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References

1. Dong, Z., Wang, F., Reese, T. G., Bilgic, B., & Setsompop, K. (2020). Echo planar time‐resolved imaging with subspace reconstruction and optimized spatiotemporal encoding. Magnetic resonance in medicine, 84(5), 2442-2455. doi: 10.1002/mrm.28295 [doi]
2. Feng, L., Wen, Q., Huang, C., Tong, A., Liu, F., & Chandarana, H. (2020). GRASP‐Pro: imProving GRASP DCE‐MRI through self‐calibrating subspace‐modeling and contrast phase automation. Magnetic resonance in medicine, 83(1), 94-108. https://doi.org/10.1002/mrm.27903 [doi]
3. Goldstein, T., & Osher, S. (2009). The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), 323-343. https://doi.org/10.1137/080725891 [doi]
4. Lustig, M., Donoho, D., & Pauly, J. M. (2007). Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 58(6), 1182-1195. https://doi.org/10.1002/mrm.21391 [doi]
5. Hoyer, P. O. (2004). Non-negative matrix factorization with sparseness constraints. Journal of machine learning research, 5(Nov), 1457-1469. https://doi.org/10.48550/arXiv.cs/0408058 [doi]
6. Hurley, N., & Rickard, S. (2009). Comparing measures of sparsity. IEEE Transactions on Information Theory, 55(10), 4723-4741. DOI: 10.1109/TIT.2009.2027527 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/568-06-009