361-06-004 ISMRM Abstract

Accelerated MR Parameter Mapping using A Generative Network Representation

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Acquisition & Reconstruction - Quantitative Imaging: relaxometry, multi-parametric, MR Fingerprinting, and synthetic MRI

361-06-004 · Quantitative Imaging: Head and Neck II · Monday, 11 May, 5:05 PM–6:00 PM · Digital Posters Row B

Keywords: Image Reconstruction Quantitative MRI T1 T2 Mapping Physics-driven Deep Learning Self-supervised learning

Accepted

Yuanmin Miao¹, Ruiyang Zhao², Fan Lam², Xi Peng ^1,3

¹Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, United States of America

²Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, United States of America

³Department of Radiology, University of Iowa, Iowa City, United States of America

Presenting Author: Xi Peng

Synopsis

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References

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8. Peng X, Lam F. A Generative Subspace Model for High-dimensional MR Imaging. the 31st Annual Meeting of ISMRM, Toronto, Canada, 2023, p0860. https://doi.org/10.58530/2023/0860 [doi]

9. 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. Magnetic resonance in medicine. 2014 Mar;71(3):990-1001. https://doi.org/10.1002/mrm.24751 [doi]

10. Yaman B, Hosseini SA, Akçakaya M. Zero-shot self-supervised learning for MRI reconstruction. arXiv preprint arXiv:2102.07737. 2021 Feb 15. https://doi.org/10.48550/arXiv.2102.07737 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/361-06-004