Literature DB >> 32857462

Automated vessel diameter quantification and vessel tracing for OCT angiography.

Wei Wei1, Qinqin Zhang1, Samuel G Rayner1,2, Wan Qin1, Yuxuan Cheng1, Fupeng Wang1, Ying Zheng1, Ruikang K Wang1.   

Abstract

Optical coherence tomography angiography (OCTA) is capable of non-invasively imaging the vascular networks within circulatory tissue beds in vivo. Following improvements in OCTA image quality, it is now possible to extract vascular parameters from imaging data to potentially facilitate the diagnosis and treatment of human disease. In this paper, we present a method for automated mapping of vessel diameter down to the individual capillary level, through gradient-guided minimum radial distance (MRD). During validation using well-characterized microfluidic flow phantoms, this method demonstrated superior consistency and a nearly threefold decrease in error when compared to currently accepted techniques. In addition, the MRD technique exhibited a high tolerance to rotation of the vasculature pattern. We also incorporated a modified A* path searching algorithm to trace vessel branches and calculate the diameter of each branch from the OCTA images. After validation in vitro, we applied these algorithms to the in vivo setting through analysis of mouse cortical vasculature. Our algorithm returned results that followed Murray's law, until reaching the capillary level, agreeing well with known physiological data. From our tracing process, vessel tortuosity and branching angle could also be measured. Our techniques provide a platform for the automated evaluation of the vasculature and may aid in diagnosis of vascular diseases, especially those resulting in regional early-stage morphological changes.
© 2020 Wiley-VCH GmbH.

Entities:  

Keywords:  GUI analysis platform; OCT; OCT angiography; branching angle; phantom validation; vasculature quantification; vessel diameter; vessel tortuosity; vessel tracing

Mesh:

Year:  2020        PMID: 32857462      PMCID: PMC7857721          DOI: 10.1002/jbio.202000248

Source DB:  PubMed          Journal:  J Biophotonics        ISSN: 1864-063X            Impact factor:   3.207


  33 in total

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Journal:  Neurobiol Aging       Date:  2018-06-22       Impact factor: 4.673

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Authors:  Vivek J Srinivasan; Harsha Radhakrishnan; Eng H Lo; Emiri T Mandeville; James Y Jiang; Scott Barry; Alex E Cable
Journal:  Biomed Opt Express       Date:  2012-02-24       Impact factor: 3.732

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Journal:  J Gen Physiol       Date:  1926-07-20       Impact factor: 4.086

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Journal:  Sci Rep       Date:  2018-03-07       Impact factor: 4.379

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  1 in total

1.  Multi-step deep neural network for identifying subfascial vessels in a dorsal skinfold window chamber model.

Authors:  Xuelin Xu; Yi Shen; Li Lin; Lisheng Lin; Buhong Li
Journal:  Biomed Opt Express       Date:  2021-12-21       Impact factor: 3.732

  1 in total

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