Denoising OCT videos based on temporal redundancy.

Autor: Richer E; Department of Computer Engineering and Software Engineering, École Polytechnique de Montréal, Montreal, QC, H3T 1J4, Canada.; Maisonneuve-Rosemont Hospital Research Center, Montreal, QC, H1T 2M4, Canada., Solano MM; Maisonneuve-Rosemont Hospital Research Center, Montreal, QC, H1T 2M4, Canada.; Department of Ophthalmology, Université de Montréal, Montreal, QC, H3T 1P1, Canada., Cheriet F; Department of Computer Engineering and Software Engineering, École Polytechnique de Montréal, Montreal, QC, H3T 1J4, Canada., Lesk MR; Maisonneuve-Rosemont Hospital Research Center, Montreal, QC, H1T 2M4, Canada.; Department of Ophthalmology, Université de Montréal, Montreal, QC, H3T 1P1, Canada., Costantino S; Maisonneuve-Rosemont Hospital Research Center, Montreal, QC, H1T 2M4, Canada. santiago.costantino@umontreal.ca.; Department of Ophthalmology, Université de Montréal, Montreal, QC, H3T 1P1, Canada. santiago.costantino@umontreal.ca.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Mar 19; Vol. 14 (1), pp. 6605. Date of Electronic Publication: 2024 Mar 19.
DOI: 10.1038/s41598-024-56935-0
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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