Section 1. Introduction
Microvasculature, including the capillaries, arterioles and venules, is a significant functional component of the human body. Many diseases present abnormalities in the microvasculature due to changes in tissue function. Imaging the microvasculature can aid the assessment of diseases and provide feedback for optimising treatment outcomes. While conventional optical coherence tomography (OCT) can provide high-resolution images of tissue structures in vivo, it cannot directly distinguish the microvasculature from other tissue.
To map and quantify the microvasculature for assessing diseases, we have developed functional OCT imaging by examining the changes to the OCT signal over time. We configure the scan pattern of the OCT scanner to acquire densely sampled B-scans or repetitive B-scans from the same tissue location. The change of OCT intensity in these B scans is calculated from which a 2-D en face maximum intensity projection (MIP) image of the microvasculature is generated. We use the MIP image with an automatic algorithm we have developed to quantify the area density and diameters of the vessels as an indicator of the tissue status.
Section 2. Methods:
The microvasculature is segmented from the 3-D OCT scan using speckle decorrelation[1, 2]. The segmentation algorithm computes a correlation map between each pair of adjacent or repetitive B-scans, using normalised cross-correlation. The computation is performed for all B-scans in the 3-D OCT scan to yield a 3-D correlation map. The low correlation (i.e., high decorrelation) regions are identified as blood vessels, since the blood flow causes rapid changes in the OCT signal and, thus, high decorrelation. Large correlation (i.e., low decorrelation) values indicate stationary tissue.
To reduce artificial decorrelation due to tissue bulk motion, each pair of adjacent B-scans is aligned using a cross-correlation, intensity-based registration algorithm prior to calculation of the correlation map. After calculation, the shape of the vessels is corrected using a fiducial marker-based registration algorithm. Vessel visualisation is performed using a 2-D en face MIP image calculated from the 3-D correlation map. First, the surface of the tissue is automatically extracted using a Canny edge detector. The MIP is generated over the en face plane, including data to a certain depth (∼600 μm) below the skin surface. In addition, a 2-D depth-encoded en face vessel image is produced by thresholding the correlation values and colour-coding vessel pixels by their depth.
The area density of microvasculature is measured by thresholding each MIP image to identify locations within the vasculature. The percentage of total surface area of vasculature per unit area of tissue is then computed as an indicator of vessel density. To measure blood vessel diameter, the MIP of the 3-D correlation map is thresholded and skeletonised. The branch points (i.e., bifurcation points) of the skeleton are subsequently identified and eliminated. This step decomposes the vasculature skeleton into distinct vessel segments, with the skeleton points marking the centre line of each section of vessel. At each skeleton point, the orientation of the vessel is calculated by finding the line of best fit to skeleton points within a small circular neighborhood. The vessel diameter at each point is estimated by tracing a line perpendicular to the vessel orientation, identifying the edges of the vessel by the rapid increase in speckle correlation.
Section 3. Microvasculature imaging quick links
1) Key applications
2) Key researchers
- M. Liew et al., “In vivo assessment of human burn scars through automated quantification of vascularity using optical coherence tomography,” J. Biomed. Opt. 18(6), 061213 (2013).
- Enfield, E. Jonathan, and M. Leahy, “In vivo imaging of the microcirculation of the volar forearm using correlation mapping optical coherence tomography (cmOCT),” Biomed. Opt. Express 2(5), 1184-1193 (2011).
- M. Liew et al., “Motion correction of in vivo three-dimensional optical coherence tomography of human skin using a fiducial marker,” Biomed. Opt. Express 3(8), 1774-1786 (2012).
- Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. (6), 679-698 (1986).
- Ogniewicz and M. Ilg, “Voronoi skeletons: Theory and applications,” 63-69, 1992.