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Skeletonization algorithm-based blood vessel quantification using in vivo 3D photoacoustic imaging
K M Meiburger1, S Y Nam, E Chung
1Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino, Italy.
Physics in Medicine and Biology
|October 27, 2016
Summary
This study introduces a novel skeletonization algorithm for analyzing blood vessel structure using non-invasive 3D photoacoustic imaging. The method quantifies vascular changes after burn surgery, offering insights into disease progression without contrast agents.
Area of Science:
- Biomedical Engineering
- Medical Imaging
- Vascular Biology
Background:
- Blood vessels are crucial for nutrient and oxygen delivery, and their abnormalities are indicators of diseases like cancer and ischemia.
- Existing imaging techniques for blood vessels often involve high costs, contrast agents, or ionizing radiation.
- Photoacoustic imaging offers high contrast and spectroscopic specificity with ultrasound's spatial resolution, using optical absorption for contrast.
Purpose of the Study:
- To develop and validate a skeletonization algorithm for quantitative analysis of vascular architecture from non-invasive 3D photoacoustic images.
- To assess microenvironmental changes in the vascular network following burn surgery using this technique.
- To evaluate the feasibility of characterizing vascular networks without exogenous contrast agents.
Main Methods:
- Acquisition of 3D photoacoustic images from rats before and after burn surgery.
- Application of a skeletonization technique involving a vesselness filter and medial axis extraction.
- Calculation of six vascular parameters: number of vascular trees (NT), vascular density (VD), number of branches (NB), 2D distance metric (DM), inflection count metric (ICM), and sum of angles metric (SOAM).
Main Results:
- The skeletonization algorithm successfully extracted vessel structures from 3D photoacoustic data.
- Four out of six vascular parameters (VD, NB, DM, ICM) showed statistically significant differences (p < 0.05) when comparing burn-affected areas to healthy areas and pre- to post-surgery.
- The study demonstrated quantitative characterization of the vascular network without contrast agents.
Conclusions:
- The proposed skeletonization algorithm enables quantitative assessment of vascular networks from non-invasive 3D photoacoustic images.
- This approach can effectively detect and quantify microenvironmental vascular changes associated with disease progression, such as post-burn injury.
- The method provides a valuable tool for vascular architectural analysis without the need for contrast agents, reducing costs and potential side effects.
