Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction
A novel method was developed for fast, high-SNR spatiospectral reconstruction from noisy MRSI data. We expect our method to be useful for enhancing the quality of MRSI or other high-dimensional spatiospectral imaging data.
Authors
- Yahang Li (ORCID: https://orcid.org/0000-0001-6206-8228)
- Graham Huesmann (ORCID: https://orcid.org/0000-0002-9120-9867)
- A Henning (ORCID: https://orcid.org/0000-0002-2267-4861)
- Aaron Anderson (ORCID: https://orcid.org/0000-0003-0440-3416)
- Loreen Ruhm (ORCID: https://orcid.org/0000-0001-7217-2993)
- Paul M. Arnold (ORCID: https://orcid.org/0000-0002-4622-7695)
- Fan Lam (ORCID: https://orcid.org/0000-0002-4124-0663)
- Zepeng Wang (ORCID: https://orcid.org/0000-0001-8877-5270)
- Ruiyang Zhao (ORCID: https://orcid.org/0009-0007-4048-6227)
Institutions
- Carle Foundation Hospital (US)
- University of Illinois Urbana-Champaign (US)
- Southwestern Medical Center (US)
- Max Planck Institute for Biological Cybernetics (DE)
- Advanced Imaging Research (United States) (US)
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2024-09-04
- DOI
- https://doi.org/10.1002/mrm.30276
- Citations
- 3
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Science Foundation
- Cancer Prevention and Research Institute of Texas
- National Institutes of Health
- University of Illinois at Urbana-Champaign
- National Institute of General Medical Sciences
- National Institute of Biomedical Imaging and Bioengineering
- Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign