Deep-learning–based optical coherence tomography reconstruction for high-speed and contrast morphology and vasculature imaging
We proposed an effective approach to improve the OCT image quality while maintaining the high acquisition speed using the DNN-based architecture, enabling simultaneous benefits of high imaging speed and enhanced sensitivity.
Authors
- Mirza Faisal Beg (ORCID: https://orcid.org/0000-0001-6706-8630)
- Zaid Mammo (ORCID: https://orcid.org/0000-0001-9230-9643)
- Da Ma (ORCID: https://orcid.org/0000-0001-8628-3029)
- Myeong Jin Ju
- Yudan Chen
- Shuo Chen
- Jun Song
Institutions
- University of British Columbia (CA)
- Simon Fraser University (CA)
- Wake Forest University (US)
Publication Details
- Journal
- Journal of Biomedical Optics
- Published
- 2026-02-04
- DOI
- https://doi.org/10.1117/1.jbo.31.2.025001
- Primary Topic
- Optical Coherence Tomography Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00