Abstract

We propose a Doppler optical micro-angiography (DOMAG) method to image flow velocities of the blood flowing in functional vessels within microcirculatory tissue beds in vivo. The method takes the advantages of recently developed optical micro-angiography (OMAG) technology, in which the endogenous optical signals backscattered from the moving blood cells are isolated from those originated from the tissue background, i.e., the tissue microstructures. The phase difference between adjacent A scans of OMAG flow signals is used to evaluate the flow velocity, similar to phase-resolved Doppler optical coherence tomography (PRDOCT). To meet the requirement of correlation between adjacent A scans in using the phase resolved technique to evaluate flow velocity, an ideal tissue-sample background (i.e., optically homogeneous tissue sample) is digitally reconstructed to replace the signals that represent the heterogeneous features of the static sample that are rejected in the OMAG flow images. Because of the ideal optical-homogeneous sample, DOMAG is free from the characteristic texture pattern noise due to the heterogeneous property of sample, leading to dramatic improvement of the imaging performance. A series of phantom flow experiments are performed to evaluate quantitatively the improved imaging performance. We then conduct in vivo experiments on a mouse brain to demonstrate that DOMAG is capable of quantifying the flow velocities within cerebrovascular network, down to capillary level resolution. Finally, we compare the in vivo imaging performance of DOMAG with that of PRDOCT, and show that DOMAG delivers at least 15-fold increase over the PRDOCT method in terms of the lower limit of flow velocity that can be detected.

Highlights

  • Optical coherence tomography (OCT) [1,2] is a non-invasive imaging technology, capable of providing high resolution, depth resolved cross-sectional images of highly scattering sample, such as biological tissue, and is attracting more and more attention for both the medical and non-medical imaging applications since it was first reported in early 1990s [3]

  • The phaseresolved Doppler optical coherence tomography (PRDOCT) method is of high resolution and high sensitivity to the blood flow, its imaging performance is greatly deteriorated by at least two factors: 1) characteristic texture pattern artifact, which is caused by optical heterogeneity of the sample [15], and 2) phase instability that is caused by the sample motion artifacts [16]

  • 4.3 Comparison between Doppler optical micro-angiography (DOMAG) and PRDOCT for in vivo imaging. Because both the DOMAG and PRDOCT methods are capable of providing the velocity information for the blood flows within the living biological tissue, here we provide a comparison between these two techniques for the cases of in vivo imaging

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Summary

Introduction

Optical coherence tomography (OCT) [1,2] is a non-invasive imaging technology, capable of providing high resolution, depth resolved cross-sectional images of highly scattering sample, such as biological tissue, and is attracting more and more attention for both the medical and non-medical imaging applications since it was first reported in early 1990s [3]. This correlation requirement makes extraction of the blood flow velocities in OMAG difficult because in the OMAG flow image, the regions that are occupied by the microstructural signals are rejected by OMAG, leading to that the correlation between adjacent A scans in these regions are totally lost To overcome this problem, we digitally reconstruct an ideal static background tissue that is totally optically homogeneous to replace the real heterogeneous tissue sample in OMAG. This ideal background tissue provides a constant background signal that makes the adjacent A-scans totally correlated, leading to a dramatic increase of the phase signal to noise ratio (SNR) for the phase-resolved signals that represent flow velocities.

System setup
Theoretical analyses
Results and discussion
In vivo experiments
Comparison between DOMAG and PRDOCT for in vivo imaging
Conclusions
Full Text
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