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.
Highlights
Optical coherence tomography (OCT) is an imaging modality with a high resolution.[1]
In intravascular OCT (IVOCT) images, different tissue types can be recognized,[4] e.g., the fibrous tissue is bright, both lipid-rich and calcified plaque appear as low intensities, while the border of the former tends to be diffused and the latter is sharper
We describe the implementation of the DR approach for the estimation of the attenuation coefficient in IVOCT images and extend it to estimate a backscatter term
Summary
Optical coherence tomography (OCT) is an imaging modality with a high resolution.[1]. To achieve automated analysis of IVOCT images for diagnosis and risk assessment, much attention has been spent on the. Based on the simplified model, one of the commonly used approaches for the estimation of the attenuation is the least square fitting either an exponential curve to a one-dimensional (1-D) OCT signal or a linear function to the logarithm of 1-D. The curve-fitting (CF) attenuation has been widely used to quantify biological tissue such as bladder urothelial carcinoma,[22] human skin,[23] nasopharyngeal carcinoma cells,[24] collagen content in ovarian tissue,[25] breast tissue,[26] and human atrial tissue.[27] for the analysis of tissue in the Journal of Biomedical Optics
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