352-01-016 / 352-01-016 ISMRM Abstract

Echo-Dependent Phase-Corrected Physics-Driven Deep Learning for Non-Cartesian Multi-Echo fMRI Reconstruction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Acquisition & Reconstruction - Artifacts and Correction Strategies

352-01-016 · New Methods: Acquisition and Reconstruction · Monday, 11 May, 8:20 AM–9:56 AM · Power Pitch Theatre 2

Keywords: Physics-driven Deep Learning Multi-echo fMRI Deep learning-based phase correction Low-pass filtering Non-Cartesian reconstruction

Accepted

Mahdi Saberi^1,2, Zidan Yu³, Christoph A Rettenmeier⁴, Victor A Stenger⁴, Mehmet Akcakaya ^1,2

¹Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, United States of America

²Center for Magnetic Resoance Research, University of Minnesota, Minneapolis, United States of America

³Philips, Best, Netherlands

⁴Department of Medicine, University of Hawaii, Honolulu, United States of America

Presenting Author: Mehmet Akcakaya

Synopsis

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References

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8. Fessler, Jeffrey A., et al. "Toeplitz-based iterative image reconstruction for MRI with correction for magnetic field inhomogeneity." IEEE Transactions on Signal Processing 53.9 (2005): 3393-3402. doi:10.1109/TSP.2005.853152 [doi]

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11. Yaman, Burhaneddin, et al. "Multi‐mask self‐supervised learning for physics‐guided neural networks in highly accelerated magnetic resonance imaging." NMR in Biomedicine 35.12 (2022): e4798, PMID: 35789133 [pmid]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/352-01-016