Reduction of photobleaching effects in photoacoustic imaging using noise agnostic, platform-flexible deep-learning methods
We demonstrate the potential of a platform-flexible generative deep learning-based approach to mitigate the effects of photobleaching in PA imaging by enhancing signal-to-noise ratio from single pulse-illuminated data, thereby improving image quality and preserving contrast in real time.
Authors
- Christopher Nguyen (ORCID: https://orcid.org/0000-0003-1475-2329)
- Avijit Paul
- Tayyaba G. Hasan (ORCID: https://orcid.org/0000-0003-0871-6057)
- Srivalleesha Mallidi (ORCID: https://orcid.org/0000-0003-1547-1764)
Institutions
- Tufts University (US)
- Harvard University (US)
- Massachusetts General Hospital (US)
Publication Details
- Journal
- Journal of Biomedical Optics
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1117/1.jbo.30.s3.s34102
- Citations
- 3
- Primary Topic
- Photoacoustic and Ultrasonic Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- National Institutes of Health