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Open AccessFeature PaperArticle

Mean-Subtraction Method for De-Shadowing of Tail Artifacts in Cerebral OCTA Images: A Proof of Concept

1
School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea
2
Biomedical Engineering Research Center, Asan Institute for Life Science, Asan Medical Center, Seoul 05505, Korea
3
Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
4
Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 05505, Korea
*
Author to whom correspondence should be addressed.
Materials 2020, 13(9), 2024; https://doi.org/10.3390/ma13092024
Received: 19 March 2020 / Revised: 22 April 2020 / Accepted: 23 April 2020 / Published: 26 April 2020
(This article belongs to the Special Issue Advances in Optical Sensors for Biomedical Applications)
When imaging brain vasculature with optical coherence tomography angiography (OCTA), volumetric analysis of cortical vascular networks in OCTA datasets is frequently challenging due to the presence of artifacts, which appear as multiple-scattering tails beneath superficial large vessels in OCTA images. These tails shadow underlying small vessels, making the assessment of vascular morphology in the deep cortex difficult. In this work, we introduce an image processing technique based on mean subtraction of the depth profile that can effectively reduce these tails to better reveal small hidden vessels compared to the current tail removal approach. With the improved vascular image quality, we demonstrate that this simple method can provide better visualization of three-dimensional vascular network topology for quantitative cerebrovascular studies. View Full-Text
Keywords: optical coherence tomography; OCT angiography; tail artifact; mean-subtraction optical coherence tomography; OCT angiography; tail artifact; mean-subtraction
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Choi, W.J.; Paulson, B.; Yu, S.; Wang, R.K.; Kim, J.K. Mean-Subtraction Method for De-Shadowing of Tail Artifacts in Cerebral OCTA Images: A Proof of Concept. Materials 2020, 13, 2024.

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