Skeletonization Based Blood Vessel Quantification Algorithm for In Vivo Photoacoustic 3D Images

Abstract

This chapter (the contents of this chapter build upon the paper: K.M. Meiburger, S.Y. Nam, E. Chung, L.J. Suggs, S.Y. Emelianov, and F. Molinari, “Skeletonization algorithm-based blood vessel quantification using in vivo 3D photoacoustic imaging”, In: Physics in Medicine and Biology, 61(22), 2016) closes the second section of this work, which is based on emphasizing quantitative imaging techniques for the assessment of architectural parameters of vasculature that can be extracted from 3D volumes. A skeletonization technique for the quantitative assessment of vascular architecture in burn wounds was developed and validated using completely non-invasive photoacoustic imaging, thus not requiring any contrast agent administration. It was shown how this technique can provide quantitative information about the vascular network from photoacoustic 3D images that can distinguish healthy from diseased tissue. Blood vessels are the only system to provide nutrients and oxygen to every part of the body. Many diseases have significant effects on blood vessel formation, so the vascular network can be a cue to assess malicious tumor and ischemic tissues. Various imaging techniques can visualize blood vessel structure, but their applications are often constrained by expensive costs, contrast agents, ionizing radiations, or a combination of the above. Photoacoustic imaging combines the high-contrast and spectroscopic-based specificity of optical imaging with the high spatial resolution of ultrasound imaging, and image contrast depends on optical absorption. This enables the detection of light absorbing chromophores such as hemoglobin with a greater penetration depth compared to purely optical techniques. A skeletonization algorithm for vessel architectural analysis using non-invasive photoacoustic 3D images acquired without the administration of any exogenous contrast agents is presented in this chapter. 3D photoacoustic images were acquired on rats (n $=4$) in two different time points: before and after a burn surgery. A skeletonization technique based on the application of a vesselness filter and medial axis extraction is proposed to extract the vessel structure from the image data and 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)) were calculated from the skeleton. The parameters were compared (1) in locations with and without the burn wound on the same day and (2) in the same anatomic location before (control) and after the burn surgery. Four out of the six descriptors were statistically different (VD, NB, DM, ICM, $p<0.05$) when comparing two anatomic locations on the same day and when considering the same anatomic location at two separate times (i.e., before and after burn surgery). The study demonstrates how it is possible to obtain quantitative characterization of the vascular network from 3D photoacoustic images without any exogenous contrast agent which can assess microenvironmental changes related to disease progression.