Emmanuelle Richer, Marissé Masís Solano, Farida Cheriet, Mark R. Lesk and Santiago Costantino
Article (2024)
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Abstract
The identification of eye diseases and their progression often relies on a clear visualization of the anatomy and on different metrics extracted from Optical Coherence Tomography (OCT) B-scans. However, speckle noise hinders the quality of rapid OCT imaging, hampering the extraction and reliability of biomarkers that require time series. By synchronizing the acquisition of OCT images with the timing of the cardiac pulse, we transform a low-quality OCT video into a clear version by phase-wrapping each frame to the heart pulsation and averaging frames that correspond to the same instant in the cardiac cycle. Here, we compare the performance of our one-cycle denoising strategy with a deep-learning architecture, Noise2Noise, as well as classical denoising methods such as BM3D and Non-Local Means (NLM). We systematically analyze different image quality descriptors as well as region-specific metrics to assess the denoising performance based on the anatomy of the eye. The one-cycle method achieves the highest denoising performance, increases image quality and preserves the high-resolution structures within the eye tissues. The proposed workflow can be readily implemented in a clinical setting.
Uncontrolled Keywords
biomedical engineering; computer science; imaging techniques; translational research
Subjects: |
1900 Biomedical engineering > 1900 Biomedical engineering 2500 Electrical and electronic engineering > 2500 Electrical and electronic engineering |
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Department: | Department of Computer Engineering and Software Engineering |
Funders: | Canadian Institutes of Health Research, Canadian Space Agency, Fonds de Recherche du Québec - Santé, Glaucoma Research Society of Canada, Institut de Valorisation des données (IVADO), OPSIDIAN |
PolyPublie URL: | https://publications.polymtl.ca/57883/ |
Journal Title: | Scientific Reports (vol. 14) |
Publisher: | Nature |
DOI: | 10.1038/s41598-024-56935-0 |
Official URL: | https://doi.org/10.1038/s41598-024-56935-0 |
Date Deposited: | 28 Mar 2024 15:20 |
Last Modified: | 16 Feb 2025 11:12 |
Cite in APA 7: | Richer, E., Solano, M. M., Cheriet, F., Lesk, M. R., & Costantino, S. (2024). Denoising OCT videos based on temporal redundancy. Scientific Reports, 14, 6605 (12 pages). https://doi.org/10.1038/s41598-024-56935-0 |
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