461-02-013 ISMRM Abstract

Population-prior-assisted Implicit Neural MRI Reconstruction for Improved Generalization Across Undersampling Patterns

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Acquisition & Reconstruction - AI methods

461-02-013 · AI To Make Protocols, Plan, QC, and Correct Motion · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row B

Keywords: Image Reconstruction Unsupervised learning Accelerated MRI reconstruction Implicit Neural Representation Generalization

Accepted

Chushu Shen ^1,2, Hengjie Liu³, Dan Ruan^4,5,6, Debiao Li^1,2

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, United States of America

²Department of Bioengineering, University of California Los Angeles, Los Angeles, United States of America

³Department of Radiation Oncology, University of California San Francisco, San Francisco, United States of America

⁴UCLA, Los Angeles, California, United States of America

⁵Radiation Oncology, UCLA Health System, Los Angeles, United States of America

⁶David Geffen School of Medicine at UCLA, Los Angeles, United States of America

Presenting Author: Chushu Shen

Synopsis

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References

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8. Knoll F, Zbontar J, Sriram A, et al. fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning. Radiol Artif Intell. 2020;2(1):e190007. doi:10.1148/ryai.2020190007 [doi]

9. Liu B, She H, Du YP. Scan-Specific Unsupervised Highly Accelerated Non-Cartesian CEST Imaging Using Implicit Neural Representation and Explicit Sparse Prior. IEEE Trans Biomed Eng. 2024;71(10):3032-3045. doi:10.1109/TBME.2024.3407092 [doi]

10. Feng J, Feng R, Wu Q, et al. Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction. IEEE Trans Med Imaging. Published online 2025:1-1. doi:10.1109/TMI.2025.3526452 [doi]

11. Hu J, Song B, Xu X, Shen L, Fessler J. Learning Image Priors Through Patch-Based Diffusion Models for Solving Inverse Problems. Adv Neural Inf Process Syst. 2025;37:1625-1660

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/461-02-013