Liu Shengnan, Sotomi Yohei, Eggermont Jeroen, Nakazawa Gaku, Torii Sho, Ijichi Takeshi, Onuma Yoshinobu, Serruys Patrick W, Lelieveldt Boudewijn P F, Dijkstra Jouke
Leiden University Medical Center, Division of Imaging Processing, Department of Radiology, Leiden, The Netherlands.
University of Amsterdam, Academic Medical Center, Amsterdam, The Netherlands.
J Biomed Opt. 2017 Sep;22(9):1-16. doi: 10.1117/1.JBO.22.9.096004.
An important application of intravascular optical coherence tomography (IVOCT) for atherosclerotic tissue analysis is using it to estimate attenuation and backscatter coefficients. This work aims at exploring the potential of the attenuation coefficient, a proposed backscatter term, and image intensities in distinguishing different atherosclerotic tissue types with a robust implementation of depth-resolved (DR) approach. Therefore, the DR model is introduced to estimate the attenuation coefficient and further extended to estimate the backscatter-related term in IVOCT images, such that values can be estimated per pixel without predefining any delineation for the estimation. In order to exclude noisy regions with a weak signal, an automated algorithm is implemented to determine the cut-off border in IVOCT images. The attenuation coefficient, backscatter term, and the image intensity are further analyzed in regions of interest, which have been delineated referring to their pathology counterparts. Local statistical values were reported and their distributions were further compared with a two-sample t-test to evaluate the potential for distinguishing six types of tissues. Results show that the IVOCT intensity, DR attenuation coefficient, and backscatter term extracted with the reported implementation are complementary to each other on characterizing six tissue types: mixed, calcification, fibrous, lipid-rich, macrophages, and necrotic core.
血管内光学相干断层扫描（IVOCT）在动脉粥样硬化组织分析中的一个重要应用是利用它来估计衰减系数和背向散射系数。这项工作旨在通过深度分辨（DR）方法的稳健实施，探索衰减系数、一个提议的背向散射项和图像强度在区分不同动脉粥样硬化组织类型方面的潜力。因此，引入DR模型来估计衰减系数，并进一步扩展以估计IVOCT图像中与背向散射相关的项，从而可以在不预先定义任何估计轮廓的情况下逐像素估计值。为了排除信号较弱的噪声区域，实施了一种自动算法来确定IVOCT图像中的截止边界。在参考其病理对应物划定的感兴趣区域中，进一步分析衰减系数、背向散射项和图像强度。报告了局部统计值，并通过双样本t检验进一步比较它们的分布，以评估区分六种组织类型的潜力。结果表明，所报告实施方法提取的IVOCT强度、DR衰减系数和背向散射项在表征六种组织类型（混合、钙化、纤维、富含脂质、巨噬细胞和坏死核心）方面相互补充。
