4178 ISMRM Abstract

Zero-FRESCO: Zero-Shot Fast REconstruction for Multi-Shot Sensitivity EnCOded Diffusion MRI

4178 · Sparse & Low-Rank Modeling & Reconstruction · Thursday, 9 May, 8:15 AM–9:15 AM · Exhibition Hall (Hall 403)

Keywords: Image Reconstruction

Accepted

Ismail Arda Vurankaya ¹, Jaejin Cho^2,3, Yohan Jun^2,3, Berkin Bilgic^2,3

¹Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey

²Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, United States of America

³Department of Radiology, Harvard Medical School, Boston, United States of America

Presenting Author: Ismail Arda Vurankaya

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References

1. J. P. Haldar, "Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS) for Constrained MRI," in IEEE Transactions on Medical Imaging, vol. 33, no. 3, pp. 668-681, March 2014, doi: 10.1109/TMI.2013.2293974. [doi]

2. B. Yaman, S. A. H. Hosseini, M. Akcakaya, “Zero-Shot Self-Supervised Learning for MRI Reconstruction”, International Conference on Learning Representations, 2021 3. C. Liao, B. Bilgic, Q. Tian, J. P. Stockmann, X. Cao, Q. Fan, S. S. Iyer, F. Wang, C. Ngamsombat, M. K. Manhard, S. Y. Huang, L. L. Wald, K Setsompop, “Distortion-free, high-isotropic-resolution diffusion MRI with gSlider BUDA-EPI and multicoil dynamic B0 shimming”, Magnetic Reson Med, vol. 86, no. 2, pp. 791-803, 2021 4. H. K. Aggarwal, M. P. Mani and M. Jacob, "MoDL: Model-Based Deep Learning Architecture for Inverse Problems," in IEEE Transactions on Medical Imaging, vol. 38, no. 2, pp. 394-405, Feb. 2019, doi: 10.1109/TMI.2018.2865356. [doi]

3. Millard C, Chiew M. A Theoretical Framework for Self-Supervised MR Image Reconstruction Using Sub-Sampling via Variable Density Noisier2Noise. IEEE Trans Comput Imaging. 2023 Jul 26;9:707-720.

4. Zhang T, Pauly JM, Vasanawala SS, Lustig M. Coil compression for accelerated imaging with Cartesian sampling. Magn Reson Med. 2013 Feb;69(2):571-82.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2024/4178