Volumetric absolute blood flow measurement with fully connected vasculature network using Doppler optical coherence tomography

Ang Li; Congwu Du; Yingtian Pan

doi:10.1117/12.2510414

22 February 2019 Volumetric absolute blood flow measurement with fully connected vasculature network using Doppler optical coherence tomography

Ang Li, Congwu Du, Yingtian Pan

Author Affiliations +

Proceedings Volume 10867, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIII; 108672I (2019) https://doi.org/10.1117/12.2510414
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

Doppler optical coherence tomography (DOCT) is a promising functional imaging modality for quantitative blood flow measurement. A major limitation of DOCT is that only the axial component of flow can be measured, which is strongly influenced by the geometry of the vasculature. To overcome this challenge, we proposed a new method to retrieve absolute blood flow velocity of the vascular networks with fully restored topology. The application of open active snake can detect the skeleton of vasculature network without the need of vasculature segmentation, allowing Doppler angle to be calculated. The discontinuity of vasculature induced by Doppler angle and the limited dynamic range is corrected by a tensor voting based method, enabling the measurement of absolute blood flow velocity along each fully connected vessel branch. We present the results of in vivo cerebral blood flow (CBF) networks to demonstrate the efficacy of the proposed method.

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Ang Li, Congwu Du, and Yingtian Pan "Volumetric absolute blood flow measurement with fully connected vasculature network using Doppler optical coherence tomography", Proc. SPIE 10867, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIII, 108672I (22 February 2019); https://doi.org/10.1117/12.2510414

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KEYWORDS

Doppler tomography

Blood circulation

Doppler effect

Velocity measurements

Optical coherence tomography

3D image processing

Algorithm development

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