Quantification of microvasculature parameters based on optoacoustic angiography data

Abstract

Optoacoustic (OA) angiography allows high-contrast three-dimensional (3D) visualization of hemoglobin-containing structures ranging from micrometers to millimeters. However, due to the large amount of 3D data acquired by modern high-throughput OA systems the resulting OA vasculature images might be difficult to analyze visually. This problem is especially relevant for monitoring of angiogenesis of experimental tumors, which blood vessels tend to be smaller and more tortuous compared to vasculature of healthy tissue. In this paper a novel algorithm for OA image processing is proposed to quantify vessel structure parameters automatically. The algorithm is based on creation of vasculature graphs which parameters (lengths of branches, number of branches, etc) can serve as a numerical characterization of vasculature: vessel density, vessel length, etc. The results of testing the developed algorithm on numerical simulation phantoms and in vivo OA images of tumor models in a mouse demonstrate a statistically significant difference of all the extracted parameters for tumor and normal tissue. The results show a high potential of the proposed approach for OA angiography in different applications including clinical and experimental oncology.

Quantitative Characterization of Age-Related Changes in Peripheral Vessels of a Human Palm Using Raster-Scan Optoacoustic Angiography

“…The reconstructed 3D images were further Frangifiltered [30] using the "vesselfilter" function from the k-Wave software package [31]. In the second stage, the quantitative algorithm described in [25] was employed. A mask with a manually selected threshold (the same for all images) was constructed from the Frangifiltered OA images, converted to binary form, and then skeletonized.…”

“…In this paper, age-related changes in the vascular bed of the skin of healthy volunteers are assessed in vivo for the first time using the OA angiographic technique. As a criterion for analyzing age-related changes in the skin, various parameters of the vascular network of the skin were calculated using a recently developed [25] and successfully applied in [26] algorithm for processing the three-dimensional OA images based on graph representation of the vascular net. The proposed algorithm was used to calculate the parameters characterizing the average density of the vessels, the length of the vessels, and the branching of the vessels on the basis of a graph constructed from a skeletonized image.…”

Combined Fluorescence and Optoacoustic Imaging for Monitoring Treatments against CT26 Tumors with Photoactivatable Liposomes

“…A recently developed three-dimensional OA image processing algorithm [ 76 ] was applied to reconstructed and Frangi filtered OA data. After the acquisition, a two-dimensional delay-and-sum reconstruction [ 77 ] in the frequency domain [ 78 ] was applied to raw OA data consequently to B-scans in XZ direction and YZ direction.…”

“…Frangi 3D filtering enabled the separation of inextricable vascular (cylindrical) structures in a diameter range of 25, 50, 100, and 150 µm, against the background of ‘non-vascular (flat and spherical) structures. The OA reconstruction and Frangi-filtration algorithms were applied in the same way as previously reported [ 76 , 80 ]. The proposed algorithm was employed to calculate the total amount of independent tree structures per volume unit (vesselness index VI) in a tumor tissue based on a graph defined by the skeletonized image.…”

Quantum Biotechnology

“…Precision imaging tools are a key enabler of progress in biotechnology. They are used routinely for medical diagnosis of vascular diseases [116,117,118] and brain conditions [119,120,121], and for monitoring of living tissues [122,123]. By allowing the structure and activity of neural networks to be studied and controlled both in vitro and in vivo [124,125,126], they open a crucial door to understanding of brain function and brain diseases, while providing similar capability for studies of complex cancers [127].…”