SignificanceSpectroscopic analysis of optical coherence tomography (OCT) data can yield added information about the sample’s chemical composition, along with high-resolution images. Typical commercial OCT systems operate at wavelengths that may not be optimal for identifying lipid-containing samples based on absorption features.AimThe main aim of this study was to develop a 1200 nm spectroscopic OCT (SOCT) for the classification of lipid-based and water-based samples by extracting the lipid absorption peak at 1210 nm from the OCT data.ApproachWe developed a 1200 nm OCT system and implemented a signal processing algorithm that simultaneously retrieves spectroscopic and structural information from the sample. In this study, we validated the performance of our OCT system by imaging weakly scattering phantoms with and without lipid absorption features. An orthogonal projections to latent structures-discriminant analysis (OPLS-DA) model was developed and applied to classify weakly scattering samples based on their absorption features.ResultsThe OCT system achieved an axial resolution of 7.2 μm and a sensitivity of 95 dB. The calibrated OPLS-DA model on weakly scattering samples with lipid and water-based absorption features correctly classified 19/20 validation samples.ConclusionsThe 1200 nm SOCT system can discriminate the lipid-containing weakly scattering samples from water-based weakly scattering samples with good predictive ability.
Light attenuation has been used for a better understanding of plaque build-up in coronary arteries. The current analysis is only useful in diseased segments. We applied an automated detection using a deep-learning approach to identify the diseased areas. A U-net was trained to detect the lumen, the guide-wire structure, healthy vessel wall, and the diseased vessel wall. The trained network achieves an average Dice index of 0.88±0.02. Applying it to all images of the testing pullbacks, diseased areas were segmented. The attenuation was estimated in this area and can be visualized in a 3-D view reconstructed using the detected lumen regions.
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.
Intravascular optical coherence tomography (IVOCT) is a new intravascular imaging modality which enables arterial
structures to be visualized at a microstructure level. The determination of these structures is currently performed
manually based on relative light intensities which is difficult because there are many factors, including the position
inside the artery and vendor of the catheter, which can influence these intensities. In this study we demonstrate how
optical attenuation and backscattering values can be computed and used as better characterizing features for different
types of atherosclerotic plaque such as fibro-atheroma, lipid-pools and calcified areas. To validate the method, different
plaque components are segmented in multiple IVOCT pullback runs using matching histology-data. The optical
attenuation, backscattering and light intensity features of the segmented regions are then automatically extracted and
analyzed for their entropy with regards to tissue characterization. The results of the validation analysis show that the
computed attenuation and backscattering measurements are in agreement with those published in literature and that
especially attenuation is a more robust feature than light intensity when it comes to tissue characterization. As a practical
application we show how attenuation and backscattering can be used to quickly determine the presence of lipid or
calcified plaques which can be important factors to determine patient treatment. Based on these findings we intend to
develop a fully automatic tissue characterization method for IVOCT.
Intravascular optical coherence tomography (IVOCT) is an imaging technique that is used to analyze the underlying cause of cardiovascular disease. Because a catheter is used during imaging, the intensities can be affected by the catheter position. This work aims to analyze the effect of the catheter position on IVOCT image intensities and to propose a compensation method to minimize this effect in order to improve the visualization and the automatic analysis of IVOCT images. The effect of catheter position is modeled with respect to the distance between the catheter and the arterial wall (distance-dependent factor) and the incident angle onto the arterial wall (angle-dependent factor). A light transmission model incorporating both factors is introduced. On the basis of this model, the interaction effect of both factors is estimated with a hierarchical multivariant linear regression model. Statistical analysis shows that IVOCT intensities are significantly affected by both factors with p<0.001, as either aspect increases the intensity decreases. This effect differs for different pullbacks. The regression results were used to compensate for this effect. Experiments show that the proposed compensation method can improve the performance of the automatic bioresorbable vascular scaffold strut detection.
Currently two commercial intravascular optical coherence tomography (IVOCT) systems are available: Illumien Optis from St. Jude Medical (SJM) and Lunawave from Terumo. Both systems store the light intensity data in a raw vendor specific polar format. However, whereas SJM uses 16-bits per pixel Terumo uses 8-bits meaning the intensity values are in different ranges. This complicates quantitative light intensity based analysis when comparing results based on data from both systems. Therefore, this work aims to find an intensity transformation function from Terumo’s 8-bit OFDI data to SJM’s 16-bit range. The data consists of 8 pullbacks, 4 acquired with each system in the same arteries of 2 different patents pre- and post-stenting implantation. A total of 133 matching sections without stent struts from the two sets of pullbacks were identified based on landmarks such as side-branches and calcified regions. Since the main region of interest in the image is the tissue region only the pixels within 2mm behind the lumen border are used. In order to match the SJM data range, the Terumo data was rescaled and cumulative distribution functions (CDF) were calculated based on the histogram distributions. Comparing these CDFs, the transformation function can be determined. Application of this transformation function not only improves the visual similarity of matching slices it can also be used for further quantitative analysis.
Intravascular optical coherence tomography (IVOCT) is an intravascular imaging modality which enables the visualization arterial structures at the micro-structural level. The interpretations of these structures is mainly on the basis of relative image intensities. However, even for homogeneous tissue light intensities can differ. In this study the incident light intensity is modeled to be related to the catheter position. Two factors, the distance between catheter and inner lumen wall as well as the incident angle of the light upon the lumen wall, are considered. A three-level hierarchical model is constructed to statistically validate this model to include the potential effect of different pullbacks and/or frame numbers. The model is solved using 169 images out of 9 pull-backs recorded with a St.Jude Medical IVOCT system. F-tests results indicate that both the distance and the incident angle contribute to the model statistically significantly with p < 0.001. Based on the results from the statistical analysis, a potential compensation method is introduced to normalize the IVOCT intensities for the catheter position effects and small shadows.
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