New Developments in Intracoronary Ultrasound Processing

New Developments in Intracoronary Ultrasound Processing

Christos V. Bourantas (Michailideion Cardiology Center, Greece & University of Hull, UK), Katerina Naka (Michailideion Cardiology Center, Greece), Dimitrios Fotiadis (Michailideion Cardiology Center, Greece) and Lampros Michalis (Michailideion Cardiology Center, Greece)
DOI: 10.4018/978-1-60566-314-2.ch004
OnDemand PDF Download:


Intracoronary Ultrasound (ICUS) imaging is an intravascular catheter-based technique which provides real-time, high resolution, cross-sectional images of coronary arteries. In these images the lumen, the media-adventitia border, the plaque burden and the composition of the plaque can be identified. Conventionally, ICUS border detection is performed manually. However, this process is laborious and time consuming. To enhance the clinical applicability of ICUS, several automated algorithms have been developed for fast ICUS segmentation and characterisation of the type of the plaque. In this chapter the authors present an overview on the developments in ICUS processing and they describe advanced methodologies which fuse ICUS and X-ray angiographic data in order to overcome indigenous limitations of ICUS imaging and provide complete and geometrically correct coronary reconstruction.
Chapter Preview


Accurate assessment of luminal pathology is useful for the diagnosis and treatment of coronary artery disease. The traditional method used for the depiction of coronary artery morphology is coronary angiography, which provides two–dimensional (2-D) views of the luminal silhouette. Major limitation of this modality is its inability to provide information regarding the plaque burden and the composition of the plaque, data which are useful to guide treatment and estimate prognosis.

To overcome these limitations intracoronary ultrasound (ICUS) has been introduced. ICUS requires the insertion of a catheter, within the coronary artery. At the tip of the catheter there is a transducer which transmits ultrasound signal perpendicular to its axis. There are two types of ICUS systems: the “mechanical” and the “electronic” ICUS systems. In mechanical systems a single rotating transducer, at 1800 rpm (30 revolutions per second), sweeps a high frequency (20 – 40 MHz) ultrasound signal perpendicular to the axis of the catheter, while in electronic systems there is an array of small crystals which have been programmed so that one set of crystals to transmit and the other to receive simultaneously. In both systems cross–sectional images of the coronary artery are produced by detecting the reflections of the ultrasound signal while this is passing through the vessel. As the ICUS catheter is pulled–back (either manually or by a motorized pull–back device) a series of images is generated. In each image, the luminal border, the outer vessel wall border (in the text the term media–adventitia border is used), the stent border, the plaque and the composition of the plaque can be identified and accurate measurements can be obtained (Mintz et al., 2001).

In ICUS images there are often several artefacts which may reduce the ability to identify the regions of interest (Figure 1). These artefacts include: the non–uniform rotational distortion which appears only in the mechanical ICUS systems, the ring down artefact (a bright halo surrounding the transducer) that is due to a high amplitude of the ultrasound signal, the guide wire artefact, the near field artefact, the blood speckles artefact, etc. (Nissen et al., 1993).

Figure 1.

Structures and artefacts observed in an ICUS image

Initially, ICUS border detection was performed manually. However, it became apparent that this process is laborious, time consuming and can be unreliable in the hands of inexperienced operators. Therefore, there was an emerging interest in the development of fast and accurate (semi-) automated segmentation algorithms which would enhance the clinical applicability of ICUS. These algorithms had to face several challenges mostly caused by the high noise content, the low image resolution, the non–uniform luminance and contrast as well as the presence of the above mentioned artefacts.

Another problem that needed to be addressed was the reliable identification of the type of the plaque as well as the integration of the detected ICUS borders into a 3-D object which would represent the coronary vessel. Some of the earlier work on the 3-D reconstruction and visualization of the ICUS sequence assumed that the vessels were straight. However, with this assumption, ICUS could not provide any information on the 3-D arterial geometry or the spatial orientation of the plaque onto the artery. To overcome these limitations fusion of biplane angiographic data and ICUS has been proposed.

In this chapter we attempt to present an overview of the developments in ICUS processing. This review is organised as follows: in the next section we describe the segmentation algorithms which have been introduced for ICUS border detection and plaque characterization. We also present methodologies which have been initially proposed for 3-D coronary reconstruction. In the main part of the chapter we describe novel techniques able to fuse ICUS and angiographic data in order to generate geometrically correct coronary segments. In addition, we present two advanced user–friendly systems, that incorporate these data fusion techniques and allow reliable and comprehensive coronary representation with the general goal to show future trends and interesting potentialities of these systems.

Key Terms in this Chapter

B-splines: Parametrical curves which are described by the following formula:, 4.10where, u is B-spline’s knot vector, Ni,p(u) is B-spline’s basis functions and is the sum of the control points that have been used to approximate spline’s morphology.

Vulnerable Plaque: Atherosclerotic plaque which is prone to thrombosis or has high probability of undergoing rapid progression and causing coronary events.

Neural Network: A modelling technique based on the observed behaviour of biological neurons and used to mimic the performance of a system. It consists of a set of elements (neurons) that start out connected in a random pattern, and, based upon operational feedback, are modelled into the pattern required to generate the optimal results.

Drug Eluting Stent: A metal tube, which is placed into diseased vessels to keep them open and releases a drug that blocks cells’ proliferation

Neointima Coverage: A new or thickened layer of arterial intima formed especially on a prosthesis (e.g. stents) by migration and proliferation of cells from the media.

Optical Coherence Tomography: Intravascular imaging modality which provides cross–sectional images of the vessel. It is similar to ICUS since it requires the insertion of a catheter within the artery, which transmits infrared light. The light is back–reflected as passing through the vessel. These reflections are obtained and analysed to finally generate high resolution cross–sectional images.

Frennet–Serret Formula: A mathematical formula developed by Jean Frédéric Frenet and Joseph Alfred Serret to calculate the curvature and torsion of a 3-D curve.

Complete Chapter List

Search this Book:
Editorial Advisory Board
Table of Contents
Themis P. Exarchos, Athanasios Papadopoulos, Dimitrios I. Fotiadis
Chapter 1
Ioannis Dimou, Michalis Zervakis, David Lowe, Manolis Tsiknakis
The automation of diagnostic tools and the increasing availability of extensive medical datasets in the last decade have triggered the development... Sample PDF
Computational Methods and Tools for Decision Support in Biomedicine: An Overview of Algorithmic Challenges
Chapter 2
William Hsu, Alex A.T. Bui, Ricky K. Taira, Hooshang Kangarloo
Though an unparalleled amount and diversity of imaging and clinical data are now collected as part of routine care, this information is not... Sample PDF
Integrating Imaging and Clinical Data for Decision Support
Chapter 3
Spyretta Golemati, John Stoitsis, Konstantina S. Nikita
The estimation of motion of the myocardial and arterial wall is important for the quantification of tissue elasticity and contractility and has... Sample PDF
Analysis and Quantification of Motion within the Cardiovascular System: Implications for the Mechanical Strain of Cardiovascular Structures
Chapter 4
Christos V. Bourantas, Katerina Naka, Dimitrios Fotiadis, Lampros Michalis
Intracoronary Ultrasound (ICUS) imaging is an intravascular catheter-based technique which provides real-time, high resolution, cross-sectional... Sample PDF
New Developments in Intracoronary Ultrasound Processing
Chapter 5
Stavroula Mougiakakou, Ioannis Valavanis, Alexandra Nikita, Konstantina S. Nikita
Recent advances in computer science provide the intelligent computation tools needed to design and develop Diagnostic Support Systems (DSSs) that... Sample PDF
Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images
Chapter 6
Marotesa Voultsidou, J. Michael Herrmann
Indicative features of an fMRI data set can be evaluated by methods provided by theory of random matrices (RMT). RMT considers ensembles of matrices... Sample PDF
Significance Estimation in fMRI from Random Matrices
Chapter 7
Dimitrios C. Karampinos, Robert Dawe, Konstantinos Arfanakis, John G. Georgiadis
Diffusion Magnetic Resonance Imaging (diffusion MRI) can provide important information about tissue microstructure by probing the diffusion of water... Sample PDF
Optimal Diffusion Encoding Strategies for Fiber Mapping in Diffusion MRI
Chapter 8
Dimitrios G. Tsalikakis, Petros S. Karvelis, Dimitrios I. Fotiadis
Segmentation plays a crucial role in cardiac magnetic resonance imaging (CMRI) applications, since it permits automated detection of regions of... Sample PDF
Segmentation of Cardiac Magnetic Resonance Images
Chapter 9
Katia Marina Passera, Luca Tommaso Mainardi
Image registration is the process of determining the correspondence of features between images collected at different times or using different... Sample PDF
Image Registration Algorithms for Applications in Oncology
Chapter 10
Lena Costaridou, Spyros Skiadopoulos, Anna Karahaliou, Nikolaos Arikidis, George Panayiotakis
Breast cancer is the most common cancer in women worldwide. Mammography is currently the most effective modality in detecting breast cancer... Sample PDF
Computer-Aided Diagnosis in Breast Imaging: Trends and Challenges
Chapter 11
E. Kyriacou, C.I. Christodoulou, C. Loizou, M.S. Pattichis, C.S. Pattichis, S. Kakkos
Stroke is the third leading cause of death in the Western world and a major cause of disability in adults. The objective of this work was to... Sample PDF
Assessment of Stroke by Analysing Cartoid Plaque Morphology
Chapter 12
Marios Neofytou, Constantinos Pattichis, Vasilios Tanos, Marios Pattichis, Eftyvoulos Kyriacou
The objective of this chapter is to propose a quantitative hysteroscopy imaging analysis system in gynaecological cancer and to provide the current... Sample PDF
Quantitative Analysis of Hysteroscopy Imaging in Gynecological Cancer
Chapter 13
Thomas V. Kilindris, Kiki Theodorou
Patient anatomy, biochemical response, as well functional evaluation at organ level, are key fields that produce a significant amount of multi modal... Sample PDF
Combining Geometry and Image in Biomedical Systems: The RT TPS Case
Chapter 14
Ioannis Tsougos, George Loudos, Panagiotis Georgoulias, Konstantina S. Nikita, Kiki Theodorou
Quantitative three-dimensional nuclear medical imaging plays a continuously increasing role in radionuclide dosimetry, allowing the development of... Sample PDF
Internal Radionuclide Dosimetry using Quantitative 3-D Nuclear Medical Imaging
Chapter 15
Evanthia E. Tripoliti, Dimitrios I. Fotiadis, Konstantia Veliou
Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI) modality which can significantly improve our understanding of the brain... Sample PDF
Diffusion Tensor Imaging and Fiber Tractography
Chapter 16
Anastasios Koutlas, Dimitrios I. Fotiadis
The aim of this chapter is to analyze the recent advances in image processing and machine learning techniques with respect to facial expression... Sample PDF
Image Processing and Machine Learning Techniques for Facial Expression Recognition
Chapter 17
Arcangelo Merla
This chapter presents an overview on recent developments in the field of clinical applications of the functional infrared imaging. The functional... Sample PDF
Developments and Advances in Biomedical Functional Infrared Imaging
Chapter 18
Aristotelis Chatziioannou, Panagiotis Moulos
The completion of the Human Genome Project and the emergence of high-throughput technologies at the dawn of the new millennium, are rapidly changing... Sample PDF
DNA Microarrays: Analysis and Interpretation
Chapter 19
Nikolaos Giannakeas, Dimitrios I. Fotiadis
Microarray technology allows the comprehensive measurement of the expression level of many genes simultaneously on a common substrate. Typical... Sample PDF
Image Processing and Machine Learning Techniques for the Segmentation of cDNA
Chapter 20
Petros S. Karvelis, Dimitrios I. Fotiadis
Automated chromosome analysis is now becoming routine in most human cytogenetics laboratories. It involves both processing and analysis of digital... Sample PDF
Recent Advances in Automated Chromosome Image Analysis
Chapter 21
O. Lezoray, G. Lebrun, C. Meurie, C. Charrier, A. Elmotataz, M. Lecluse
The segmentation of microscopic images is a challenging application that can have numerous applications ranging from prognosis to diagnosis.... Sample PDF
Machine Learning in Morphological Segmentation
Chapter 22
Michael Haefner, Alfred Gangl, Michael Liedlgruber, A. Uhl, Andreas Vecsei, Friedrich Wrba
Wavelet-, Fourier-, and spatial domain-based texture classification methods have been used successfully for classifying zoom-endoscopic colon images... Sample PDF
Pit Pattern Classification Using Multichannel Features and Multiclassification
Chapter 23
C. Papaodysseus, P. Rousopoulos, D. Arabadjis, M. Panagopoulos, P. Loumou
In this chapter the state of the art is presented in the domain of automatic identification and classification of bodies on the basis of their... Sample PDF
Automatic Identification and Elastic Properties of Deformed Objects Using their Microscopic Images
Chapter 24
Alexia Giannoula, Richard S.C. Cobbold
“Elastography” or “elasticity imaging” can be defined as the science and methodology of estimating the mechanical properties of a medium (including... Sample PDF
Nonlinear Ultrasound Radiation-Force Elastography
Chapter 25
Valentina Russo, Roberto Setola
The aim of this chapter is to provide an overview about models and methodologies used for the Dynamic Contrast Enhancement (DCE) analysis. DCE is a... Sample PDF
Dynamic Contrast Enhancement: Analysis's Models and Methodologies
Chapter 26
George K. Matsopoulos
The accurate estimation of point correspondences is often required in a wide variety of medical image processing applications including image... Sample PDF
Automatic Correspondence Methods towards Point-Based Medical Image Registration: An Evaluation Study
Chapter 27
Alberto Taboada-Crispi, Hichem Sahli, Denis Hernandez-Pacheco, Alexander Falcon-Ruiz
Various approaches have been taken to detect anomalies, with certain particularities in the medical image scenario, linked to other terms... Sample PDF
Anomaly Detection in Medical Image Analysis
Chapter 28
C. Delgorge-Rosenberger, C. Rosenberger
The authors present in this chapter an overview on evaluation of medical image compression. The different methodologies used in the literature are... Sample PDF
Evaluation of Medical Image Compression
Chapter 29
Charalampos Doukas, Ilias Maglogiannis
Medical images are often characterized by high complexity and consist of high resolution image files, introducing thus several issues regarding... Sample PDF
Advanced ROI Coding Techniques for Medical Imaging
Chapter 30
Farhang Sahba
Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images... Sample PDF
Segmentation Methods in Ultrasound Images
About the Editors
About the Contributors