Meiburger K M, Nam S Y, Chung E, Suggs L J, Emelianov S Y, Molinari F
Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino, Italy.
Phys Med Biol. 2016 Nov 21;61(22):7994-8009. doi: 10.1088/0031-9155/61/22/7994. Epub 2016 Oct 25.
Blood vessels are the only system to provide nutrients and oxygen to every part of the body. Many diseases can have significant effects on blood vessel formation, so that 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 either 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. We present here a skeletonization algorithm for vessel architectural analysis using non-invasive photoacoustic 3D images acquired without the administration of any exogenous contrast agents. 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 an approach 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.
血管是为身体各个部位提供营养和氧气的唯一系统。许多疾病会对血管形成产生重大影响，因此血管网络可以作为评估恶性肿瘤和缺血组织的一个线索。各种成像技术能够使血管结构可视化，但其应用常常受到高昂成本、造影剂、电离辐射或上述因素组合的限制。光声成像将光学成像的高对比度和基于光谱的特异性与超声成像的高空间分辨率相结合，且图像对比度取决于光吸收。与纯光学技术相比，这使得能够检测诸如血红蛋白等吸光发色团，且具有更大的穿透深度。我们在此展示一种用于血管结构分析的骨架化算法，该算法使用在未施用任何外源性造影剂的情况下获取的非侵入性光声三维图像。在两个不同时间点对大鼠（n = 4）进行三维光声图像采集：烧伤手术前后。提出了一种基于血管性滤波器应用和中轴线提取的骨架化技术，以从图像数据中提取血管结构，并从骨架中计算六个血管参数（血管树数量（NT）、血管密度（VD）、分支数量（NB）、二维距离度量（DM）、拐点计数度量（ICM）和角度总和度量（SOAM））。对这些参数进行了比较：（1）在同一天有烧伤创面和无烧伤创面的部位之间；（2）在烧伤手术前（对照）和烧伤手术后相同解剖位置之间。当比较同一天的两个解剖位置以及考虑两个不同时间（即烧伤手术前后）的相同解剖位置时，六个描述符中有四个在统计学上存在差异（VD、NB、DM、ICM，p < 0.05）。该研究展示了一种从三维光声图像中获取血管网络定量特征的方法，无需任何外源性造影剂，可评估与疾病进展相关的微环境变化。
