ECLARE: efficient cross-planar learning for anisotropic resolution enhancement
The use of slice profile estimation, FOV-aware resampling, and self-SR allowed ECLARE to robustly superresolve anisotropic images without the need for external training data. Future work will investigate the utility of ECLARE on other organs, species, modalities, and resolutions. Our code is open-source and available at https://www.github.com/sremedios/eclare.
Authors
- Samuel W. Remedios (ORCID: https://orcid.org/0000-0001-8634-8128)
- D H Pham
- Shuwen Wei
- Aaron Carass
- Blake E. Dewey
- Shuo Han
- Kurt G. Schilling
- Jerry L. Prince
- Jinwei Zhang
Institutions
- Johns Hopkins University (US)
- Uniformed Services University of the Health Sciences (US)
- Johns Hopkins Medicine (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2026-03-04
- DOI
- https://doi.org/10.1117/1.jmi.13.2.024001
- Primary Topic
- Advanced Image Processing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00