| Literature DB >> 28901053 |
Shengnan Liu1, Yohei Sotomi2, Jeroen Eggermont1, Gaku Nakazawa3, Sho Torii3, Takeshi Ijichi3, Yoshinobu Onuma4,5, Patrick W Serruys6, Boudewijn P F Lelieveldt1,7, Jouke Dijkstra1.
Abstract
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. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).Entities:
Keywords: attenuation coefficient; backscatter term; calcification; depth-resolved; fibrous; intravascular optical coherence tomography; lipid; necrotic core
Mesh:
Year: 2017 PMID: 28901053 DOI: 10.1117/1.JBO.22.9.096004
Source DB: PubMed Journal: J Biomed Opt ISSN: 1083-3668 Impact factor: 3.170