Shape-driven segmentation of the arterial wall in intravascular ultrasound images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach

Intravascular Ultrasound

Intravascular ultrasound (IVUS) is a cardiovascular imaging technology using a specially designed catheter with a miniaturized ultrasound probe for the assessment of vascular anatomy with detailed visualization of arterial layers. Over the past two decades, this technology has developed into an indispensable tool for research and clinical practice in cardiovascular medicine, offering the opportunity to gather diagnostic information about the process of atherosclerosis in vivo, and to directly observe the effects of various interventions on the plaque and arterial wall. This book aims to give a comprehensive overview of this rapidly evolving technique from basic principles and instrumentation to research and clinical applications with future perspectives

Optical coherence tomography for the assessment of coronary atherosclerosis and vessel response after stent implantation

Optical Coherence Tomography (OCT) is a light-based imaging modality that can provide in vivo high-resolution images of the coronary artery with a level of resolution (axial 10-20 µm) ten times higher than intravascular ultrasound. The technique, uses low-coherent near infrarred light to create high-resolution cross sectional images of the vessel. The technology refinement achieved in the last years has made this imaging modality less procedurally demanding opening its possibilities for clinical use. The present thesis provides im

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.This research was funded by Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-02G31, and the Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (FRGS Vot-4F551) for the completion of the research. The work and the contribution were also supported by the project Smart Solutions in Ubiquitous Computing Environments, Grant Agency of Excellence, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-GE-2018). Furthermore, the research is also partially supported by the Spanish Ministry of Science, Innovation and Universities with FEDER funds in the project TIN2016-75850-R

The Evolution of Data Fusion Methodologies Developed to Reconstruct Coronary Artery Geometry From Intravascular Imaging and Coronary Angiography Data: A Comprehensive Review

Understanding the mechanisms that regulate atherosclerotic plaque formation and evolution is a crucial step for developing treatment strategies that will prevent plaque progression and reduce cardiovascular events. Advances in signal processing and the miniaturization of medical devices have enabled the design of multimodality intravascular imaging catheters that allow complete and detailed assessment of plaque morphology and biology. However, a significant limitation of these novel imaging catheters is that they provide two-dimensional (2D) visualization of the lumen and vessel wall and thus they cannot portray vessel geometry and 3D lesion architecture. To address this limitation computer-based methodologies and user-friendly software have been developed. These are able to off-line process and fuse intravascular imaging data with X-ray or computed tomography coronary angiography (CTCA) to reconstruct coronary artery anatomy. The aim of this review article is to summarize the evolution in the field of coronary artery modeling; we thus present the first methodologies that were developed to model vessel geometry, highlight the modifications introduced in revised methods to overcome the limitations of the first approaches and discuss the challenges that need to be addressed, so these techniques can have broad application in clinical practice and research

Combined optical coherence tomography and intravascular ultrasound radio frequency data analysis for plaque characterization. Classification accuracy of human coronary plaques in vitro

This study was performed to characterize coronary plaque types by optical coherence tomography (OCT) and intravascular ultrasound (IVUS) radiofrequency (RF) data analysis, and to investigate the possibility of error reduction by combining these techniques. Intracoronary imaging methods have greatly enhanced the diagnostic capabilities for the detection of high-risk atherosclerotic plaques. IVUS RF data analysis and OCT are two techniques focusing on plaque morphology and composition. Regions of interest were selected and imaged with OCT and IVUS in 50 sections, from 14 human coronary arteries, sectioned post-mortem from 14 hearts of patients dying of non-cardiovascular causes. Plaques were classified based on IVUS RF data analysis (VH-IVUSTM), OCT and the combination of those. Histology was the benchmark. Imaging with both modalities and coregistered histology was successful in 36 sections. OCT correctly classified 24; VH-IVUS 25, and VH-IVUS/OCT combined, 27 out of 36 cross-sections. Systematic misclassifications in OCT were intimal thickening classified as fibroatheroma in 8 cross-sections. Misclassifications in VH-IVUS were mainly fibroatheroma as intimal thickening in 5 cross-sections. Typical image artifacts were found to affect the interpretation of OCT data, misclassifying intimal thickening as fibroatheroma or thin-cap fibroatheroma. Adding VH-IVUS to OCT reduced the error rate in this study

Coronary plaque composition as assessed by greyscale intravascular ultrasound and radiofrequency spectral data analysis

Objectives: (i) To explore the relation between greyscale intravascular ultrasound (IVUS) plaque qualitative classification and IVUS radiofrequency data (RFD) analysis tissue types; (ii) to evaluate if plaque composition as assessed by RFD analysis can be predicted by visual assessment of greyscale IVUS images. Methods: In 120 IVUS-RFD cross-sections, a sector of the plaque with homogenous tissue composition (e.g., fibrous, fibrofatty, necrotic core, and dense calcium) was selected. Two experienced observers analyzed twice the corresponding greyscale IVUS images to: (1) classify the selected sectors according to greyscale IVUS plaque type classification and (2) predict the tissue type expected in the sector by RFD analysis. Results: In the greyscale IVUS plaque type classification, the observers agreed in 90/120 sectors (κ = 0.64). Calcified, soft and mixed plaques by greyscale IVUS classification were mainly composed of dense calcium, fibrofatty, and necrotic core, respectively, in the RFD analysis. The plaques classified in greyscale IVUS as fibrous were actually fibrous tissue by IVUS RFD in only 30% of the cases. Overall, high interobserver variability in the prediction of RFD results by visual assessment of greyscale IVUS images (κ = 0.23 for observer 1 and 0.55 for observer 2) was found. Sens