ArticleBiomedical optics express2024
Accurate attenuation characterization in optical coherence tomography using multi-reference phantoms and deep learning.
Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Monte Carlo OCT simulations investigating the effect of multiple scattered light on OCT-based attenuation coefficient quantification.Biomedical optics express · 2026Article
- Article
- Advances in Technology and Applications of Optical Sensing and Imaging for Biomedicine: introduction to the feature issue.Biomedical optics express · 2025Article
- Quantitative assessment of retinal attenuation and backscattering in OCT imaging using iterative layer-based analysis.Biomedical optics express · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
The optical attenuation coefficient (AC), a crucial tissue parameter indicating the rate of light attenuation within a medium, enables quantitative analysis of tissue properties and facilitates tissue differentiation. Despite its growing clinical significance, accurate quantification of AC from optical coherence tomography (OCT) signals remains a pressing concern. This study comprehensively investigates the factors influencing the accuracy of quantitative AC extraction among existing OCT-based AC extraction algorithms. Subsequently, we propose an approach, the Multi-Reference Phantom Driven Network (MR-Net), which leverages multi-reference phantoms and deep learning to implicitly model factors affecting OCT signal propagation, thereby automatically regressing AC. Using a dataset from Intralipid and silicone-TiO
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