Estimating coronary artery lumen area with optimization-based contour detection

A modified optimization-based contour detection method was presented to compute the lumen area of the coronary artery from intravascular ultrasound (IVUS) video images. First, the search range for the artery inner wall was determined based on the continuity of IVUS video frames. Next, the internal and external energy were calculated to describe the smoothness of the arterial wall and the grayscale variation of ultrasound images, respectively. Here, a novel form of the external energy which combines the gradient and variance of the intensity of image in the radial direction was used. Finally, the minimal energy path based on the optimum contour of the artery wall was obtained using circular dynamic programming (DP). By the comparison with the typical DP procedure using the traditional external energy form, based only on the image gradient, the reliability of this modified method is considerably improved in the measurement of coronary artery lumen area.     

Intravascular ultrasound image segmentation: a three-dimensional fast-marching method based on gray level distributions

Intravascular ultrasound (IVUS) is a catheter based medical imaging technique particularly useful for studying atherosclerotic disease. It produces cross-sectional images of blood vessels that provide quantitative assessment of the vascular wall, information about the nature of atherosclerotic lesions as well as plaque shape and size. Automatic processing of large IVUS data sets represents an important challenge due to ultrasound speckle, catheter artifacts or calcification shadows. A new three-dimensional (3-D) IVUS segmentation model, that is based on the fast-marching method and uses gray level probability density functions (PDFs) of the vessel wall structures, was developed. The gray level distribution of the whole IVUS pullback was modeled with a mixture of Rayleigh PDFs. With multiple interface fast-marching segmentation, the lumen, intima plus plaque structure, and media layers of the vessel wall were computed simultaneously. The PDF-based fast-marching was applied to 9 in vivo IVUS pullbacks of superficial femoral arteries and to a simulated IVUS pullback. Accurate results were obtained on simulated data with average point to point distances between detected vessel wall borders and ground truth <0.072 mm. On in vivo IVUS, a good overall performance was obtained with average distance between segmentation results and manually traced contours <0.16 mm. Moreover, the worst point to point variation between detected and manually traced contours stayed low with Hausdorff distances <0.40 mm, indicating a good performance in regions lacking information or containing artifacts. In conclusion, segmentation results demonstrated the potential of gray level PDF and fast-marching methods in 3-D IVUS image processing.     

life Sciences - Signal-Processing Approaches to Risk Assessment in Coronary Artery Disease

This paper introduces a novel method that enables intravascular ultrasound (IVUS) detection of atherosclerotic plaque inflammation based on quantification of vasa vasorum (VV) density and perfusion. The method consists of a contrast-enhanced IVUS acquisition protocol and a series of signal/image processing techniques to detect VV in the resulting contrast-enhanced sequences     

Characterization of atherosclerotic plaques and mural thrombi with intravascular ultrasound elastography: a potential method evaluated in an aortic rabbit model and a human coronary artery

Plaque rupture is correlated with the plaque morphology, composition, mechanical properties, and with the blood pressure. Whereas the geometry can accurately be assessed with intravascular ultrasound (IVUS) imaging, intravascular elastography (IVE) is capable of extracting information on the plaque local mechanical properties and composition. This paper reports additional IVE validation data regarding reproducibility and potential to characterize atherosclerotic plaques and mural thrombi. In a first investigation, radio frequency (RF) data were acquired from the abdominal aorta of an atherosclerotic rabbit model. In a second investigation, IVUS RF data were recorded from the left coronary artery of a patient referred for angioplasty. In both cases, Galaxy IVUS scanners (Boston Scientific, Freemont, CA), equipped with 40 MHz Atlantis catheters, were used. Elastograms were computed using two methods, the Lagrangian speckle model estimator (LSME) and the scaling factor estimator (SFE). Corroborated with histology, the LSME and the SFE both clearly detected a soft thrombus attached to the vascular wall. Moreover, shear elastograms, only available with the LSME, confirmed the presence of the thrombus. Additionally, IVE was found reproducible with consistent elastograms between cardiac cycles (CCs). Regarding the human dataset, only the LSME was capable of identifying a plaque that presumably sheltered a lipid core. Whereas such an assumption could not be certified with histology, radial shear and tangential strain LSME elastograms enabled the same conclusion. It is worth emphasizing that this paper reports the first ever in vivo tangential strain elastogram with regards to vascular imaging, due to the LSME. It is concluded that the IVE was reproducible exhibiting consistent strain patterns between CCs. The IVE might provide a unique tool to assess coronary wall lesions.     

Fusion of angiography and intravascular ultrasound in vivo:establishing the absolute 3-D frame orientation

Data fusion of biplane angiography and intravascular ultrasound (IVUS) facilitates geometrically correct reconstruction of coronary vessels. The locations of IVUS frames along the catheter pullback trajectory can be identified, however the IVUS image orientations remain ambiguous. An automated approach to determination of correct IVUS image orientation in three-dimensional space is reported. Analytical calculation of the catheter twist is followed by statistical optimization determining the absolute IVUS image orientation. The fusion method was applied to data acquired in patients undergoing routine coronary intervention, demonstrating the feasibility and good performance of our approach.     

Segmentation of intravascular ultrasound images: a knowledge-based approach

Intravascular ultrasound imaging of coronary arteries provides important information about coronary lumen, wall, and plaque characteristics. Quantitative studies of coronary atherosclerosis using intravascular ultrasound and manual identification of wall and plaque borders are limited by the need for observers with substantial experience and the tedious nature of manual border detection. We have developed a method for segmentation of intravascular ultrasound images that identifies the internal and external elastic laminae and the plaque-lumen interface. The border detection algorithm was evaluated in a set of 38 intravascular ultrasound images acquired from fresh cadaveric hearts using a 30 MHz imaging catheter. To assess the performance of our border detection method we compared five quantitative measures of arterial anatomy derived from computer-detected borders with measures derived from borders manually defined by expert observers. Computer-detected and observer-defined lumen areas correlated very well (r=0.96, y=1.02x+0.52), as did plaque areas (r=0.95, y=1.07x-0.48), and percent area stenosis (r=0.93, y=0.99x-1.34.) Computer-derived segmental plaque thickness measurements were highly accurate. Our knowledge-based intravascular ultrasound segmentation method shows substantial promise for the quantitative analysis of in vivo intravascular ultrasound image data.     

Fuzzy inference-based plaque boundary extraction using image separability for intravascular ultrasound image of coronary artery

A novel plaque boundary extraction method for an intravascular ultrasound (IVUS) image employing image separability and Takagi-Sugeno's fuzzy inference model is presented. The membership functions are allocated heuristically with special consideration of the features of the IVUS image. The method has not only improved the accuracy of plaque boundary extraction but has also reduced the workload of medical doctors.     

A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images

Over the past two decades, intravascular ultrasound (IVUS) image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in catheterization procedures and in research studies. IVUS provides cross-sectional grayscale images of the arterial wall and the extent of atherosclerotic plaques with high spatial resolution in real time. In this paper, we review recently developed image processing methods for the detection of media-adventitia and luminal borders in IVUS images acquired with different transducers operating at frequencies ranging from 20 to 45 MHz. We discuss methodological challenges, lack of diversity in reported datasets, and weaknesses of quantification metrics that make IVUS segmentation still an open problem despite all efforts. In conclusion, we call for a common reference database, validation metrics, and ground-truth definition with which new and existing algorithms could be benchmarked.     

An automatic approach for artifacts detection and shadow enhancement in intravascular ultrasound images

Intravascular ultrasound (IVUS) is clinically available for visualizing coronary arteries. However, it suffers from acoustic shadow areas and ring-down artifacts as two of the common issues in IVUS images. This paper introduces an approach which can overcome these limitations. As shadow areas were displayed behind hard plaques in the IVUS grayscale images, calcified plaques were first segmented by using Otsu threshold. Then, active contour, histogram matching, and local histogram matching are implemented. In addition, a new modified circle Hough transform is introduced to remove the ring-down artifacts from IVUS images. In order to evaluate the efficacy of this new method in detection of shadow and ring-down regions, 300 IVUS images are considered. Sensitivity of 89% and specificity of 92% are achieved from a comparison in revelation of calcium along with shadow in the proposed method and virtual histology images. Also, area differences of \(5.83 \pm 3.3\) and \(5.65 \pm 2.83\) are obtained, respectively, for ring-down and shadow domain when compared to measures performed manually by a clinical expert.     

Approaching Artery Rigid Dynamics in IVUS

Tissue biomechanical properties (like strain and stress) are playing an increasing role in diagnosis and long-term treatment of intravascular coronary diseases. Their assessment strongly relies on estimation of vessel wall deformation. Since intravascular ultrasound (IVUS) sequences allow visualizing vessel morphology and reflect its dynamics, this technique represents a useful tool for evaluation of tissue mechanical properties. Image misalignment introduced by vessel–catheter motion is a major artifact for a proper tracking of tissue deformation. In this work, we focus on compensating and assessing IVUS rigid in-plane motion due to heart beating. Motion parameters are computed by considering both the vessel geometry and its appearance in the image. Continuum mechanics laws serve to introduce a novel score measuring motion reduction in in vivo sequences. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; whereas results in in vivo pullbacks show the reliability of the presented methodologies in clinical cases.      

A novel semiautomated atherosclerotic plaque characterization method using grayscale intravascular ultrasound images: comparison with virtual histology

Intravascular ultrasound (IVUS) virtual histology (VH-IVUS) is a new technique, which provides automated plaque characterization in IVUS frames, using the ultrasound backscattered RF-signals. However, its computation can only be performed once per cardiac cycle (ECG-gated technique), which significantly decreases the number of characterized IVUS frames. Also atherosclerotic plaques in images that have been acquired by machines, which are not equipped with the VH software, cannot be characterized. To address these limitations, we have developed a plaque characterization technique that can be applied in grayscale IVUS images. Our semiautomated method is based on a three-step approach. In the first step, the plaque area [region of interest (ROI)] is detected semiautomatically. In the second step, a set of features is extracted for each pixel of the ROI and in the third step, a random forest classifier is used to classify these pixels into four classes: dense calcium, necrotic core, fibrotic tissue, and fibro-fatty tissue. In order to train and validate our method, we used 300 IVUS frames acquired from virtual histology examinations from ten patients. The overall accuracy of the proposed method was 85.65% suggesting that our approach is reliable and may be further investigated in the clinical and research arena.     

Interactive virtual endoscopy in coronary arteries based on multimodality fusion

A novel approach for platform-independent virtual endoscopy in human coronary arteries is presented in this paper. It incorporates previously developed and validated methodology for multimodality fusion of two X-ray angiographic images with pullback data from intravascular ultrasound (IVUS). These modalities pose inherently different challenges than those present in many tomographic modalities that provide parallel slices. The fusion process results in a three- or four-dimensional (3-D/4-D) model of a coronary artery, specifically of its lumen/plaque and media/adventitia surfaces. The model is used for comprehensive quantitative hemodynamic, morphologic, and functional analyses. The resulting quantitative indexes are then used to supplement the model. Platform-independent visualization is achieved through the use of the ISO/IEC-standardized Virtual Reality Modeling Language (VRML). The visualization includes an endoscopic fly-through animation that enables the user to interactively select vessel location and fly-through speed, as well as to display image pixel data or quantification results in 3-D. The presented VRML virtual-endoscopy system is used in research studies of coronary atherosclerosis development, quantitative assessment of coronary morphology and function, and vascular interventions.     

A robust 3-D IVUS transducer tracking using single-plane cineangiography

During an intravascular ultrasound (IVUS) intervention, a catheter with an ultrasound transducer is introduced in the body through a blood vessel, and then, pulled back to image a sequence of vessel cross sections. Unfortunately, there is no 3-D information about the position and orientation of these cross-section planes, which makes them less informative. To position the IVUS images in space, some researchers have proposed complex stereoscopic procedures relying on biplane angiography to get two X-ray image sequences of the IVUS transducer trajectory along the catheter. To simplify this procedure, we and others have elaborated algorithms to recover the transducer 3-D trajectory with only a single view X-ray image sequence. In this paper, we present an improved method that provides both automated 2-D and 3-D transducer tracking based on pullback speed as a priori information. The proposed algorithm is robust to erratic pullback speed and is more accurate than the previous single-plane 3-D tracking methods.     

In Vivo IVUS-Based 3-D Fluid–Structure Interaction Models With Cyclic Bending and Anisotropic Vessel Properties for Human Atherosclerotic Coronary Plaque Mechanical Analysis

In this paper, a modeling approach combining in vivo intravascular ultrasound (IVUS) imaging, computational modeling, angiography, and mechanical testing is proposed to perform mechanical analysis for human coronary atherosclerotic plaques for potential more accurate plaque vulnerability assessment. A 44-slice in vivo IVUS dataset of a coronary plaque was acquired from one patient, and four 3-D models with fluid-structure interactions (FSIs) based on the data were constructed to quantify effects of anisotropic vessel properties and cyclic bending of the coronary plaque on flow and plaque stress/strain conditions. Compared to the isotropic model (model 1, no bending, no axial stretch), maximum stress-P₁ (maximum principal stress) values on the cut surface with maximum bending (where applicable) from model 2 (anisotropic, no bending, no stretch), model 3 (anisotropic, with bending, no stretch), and model 4 (anisotropic with bending and stretch) were, respectively, 63%, 126%, and 345% higher than that from model 1. Effects of cyclic bending on flow behaviors were modest (5%-15%). Our preliminary results indicated that in vivo IVUS-based FSI models with cyclic bending and anisotropic material properties could improve the accuracies of plaque stress/strain predictions and plaque vulnerability assessment. Large-scale patient studies are needed to further validate our findings.     

Evaluation of three-dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound images

Intravascular ultrasound (IVUS) provides direct depiction of coronary artery anatomy, including plaque and vessel area, which is important in quantitative studies on the progression or regression of coronary artery disease. Traditionally, these studies have relied on manual evaluation, which is laborious, time consuming, and subject to large interobserver and intraobserver variability. A new technique, called active surface segmentation, alleviates these limitations and makes strides toward routine analyses. However, for three-dimensional (3-D) plaque assessment or 3-D reconstruction to become a clinical reality, methods must be developed which can analyze many images quickly. Presented is a comparison between two active surface techniques for three-dimensional segmentation of luminal and medial-adventitial borders. The force-acceleration technique and the neighborhood-search technique accurately detected both borders in vivo (r2 = 0.95 and 0.99, Williams' index = 0.67 and 0.65, and r2 = 0.95 and 0.99, WI = 0.67 and 0.70, respectively). However, the neighborhood-search technique was significantly faster and required less computation. Volume calculations for both techniques (r2 = 0.99 and r2 = 0.99) also agreed with a known-volume phantom. Active surface segmentation allows 3-D assessment of coronary morphology and further developments with this technology will provide clinical analysis tools.     

An inverse method for imaging the local elasticity of atherosclerotic coronary plaques

The rupture of thin-cap fibroatheroma (TCFA) plaques is a major cause of acute coronary events. A TCFA has a trombogenic soft lipid core, shielded from the blood stream by a thin, possibly inflamed, stiff cap. The majority of atherosclerotic plaques resemble a TCFA in terms of overall structural composition, but have a more complex, heterogeneous morphology. An assessment of the material distribution is vital for quantifying the plaque's mechanical stability and for determining the effect of plaque-stabilizing pharmaceutical agents. We describe a new automated inverse elasticity method, intravascular ultrasound (IVUS) modulography, which is capable of reconstructing a heterogeneous Young's modulus distribution. The elastogram (i.e., spatial strain distribution) of the plaque is the input for the method, and is measured using the clinically available technique, IVUS elastography. Our method incorporates a novel divide-and-conquer strategy, allowing the reconstruction of TCFAs as well as heterogeneous plaques with localized regions of soft, weakened tissue. The method was applied to ex vivo elastograms, which were simulated from the cross sections of postmortem human coronary plaques. To demonstrate the clinical feasibility of the method, measured elastograms from human atherosclerotic coronary arteries were analyzed. One elastogram was measured in vitro; the other, in vivo. The method approximated the true Young's modulus distribution of all simulated plaques, while the in vitro reconstruction was in agreement with histology. In conclusion, the IVUS modulography in combination with the IVUS elastography has strong potential to become an all-encompassing modality for detecting plaques, for assessing the information related to their rupture-proneness, and for imaging their heterogeneous elastic material composition.     

Assessment of vulnerable plaque composition by matching the deformation of a parametric plaque model to measured plaque deformation

Intravascular ultrasound (IVUS) elastography visualizes local radial strain of arteries in so-called elastograms to detect rupture-prone plaques. However, due to the unknown arterial stress distribution these elastograms cannot be directly interpreted as a morphology and material composition image. To overcome this limitation we have developed a method that reconstructs a Young's modulus image from an elastogram. This method is especially suited for thin-cap fibroatheromas (TCFAs), i.e., plaques with a media region containing a lipid pool covered by a cap. Reconstruction is done by a minimization algorithm that matches the strain image output, calculated with a parametric finite element model (PFEM) representation of a TCFA, to an elastogram by iteratively updating the PFEM geometry and material parameters. These geometry parameters delineate the TCFA media, lipid pool and cap regions by circles. The material parameter for each region is a Young's modulus, EM, EL, and EC, respectively. The method was successfully tested on computer-simulated TCFAs (n = 2), one defined by circles, the other by tracing TCFA histology, and additionally on a physical phantom (n = 1) having a stiff wall (measured EM = 16.8 kPa) with an eccentric soft region (measured EL = 4.2 kPa). Finally, it was applied on human coronary plaques in vitro (n = 1) and in vivo (n = 1). The corresponding simulated and measured elastograms of these plaques showed radial strain values from 0% up to 2% at a pressure differential of 20, 20, 1, 20, and 1 mmHg respectively. The used/reconstructed Young's moduli [kPa] were for the circular plaque EL = 50/66, EM = 1500/1484, EC = 2000/2047, for the traced plaque EL = 25/1, EM = 1000/1148, EC = 1500/1491, for the phantom EL = 4.2/4 kPa, EM = 16.8/16, for the in vitro plaque EL = n.a./29, EM = n.a./647, EC = n.a./1784 kPa and for the in vivo plaque EL = n.a./2, EM = n.a./188, Ec = n.a./188 kPa.     

An automated method for lumen and media-adventitia border detection in a sequence of IVUS frames

In this paper, we present a method for the automated detection of lumen and media-adventitia border in sequential intravascular ultrasound (IVUS) frames. The method is based on the use of deformable models. The energy function is appropriately modified and minimized using a Hopfield neural network. Proper modifications in the definition of the bias of the neurons have been introduced to incorporate image characteristics. A simulated annealing scheme is included to ensure convergence at a global minimum. The method overcomes distortions in the expected image pattern, due to the presence of calcium, employing a specialized structure of the neural network and boundary correction schemas which are based on a priori knowledge about the vessel geometry. The proposed method is evaluated using sequences of IVUS frames from 18 arterial segments, some of them indicating calcified regions. The obtained results demonstrate that our method is statistically accurate, reproducible, and capable to identify the regions of interest in sequences of IVUS frames.     

Image-based gating of intravascular ultrasound pullback sequences

Intravascularultrasound (IVUS) sequences recorded in vivo are subject to a wide array of motion artifacts as the majority of these studies are performed within the coronary arteries of a beating heart. To eliminate these artifacts, an electrocardiogram (ECG) signal is typically used to gate (collect) those frames recorded at the points in time associated with a particular fraction of the cardiac cycle. However, this technique may be suboptimal for a number of reasons, among which is the difficulty of determining the optimal fraction at which to gate. This value is generally nonobvious. To circumvent this problem, we introduce a frame-gating method for IVUS pullbacks that mimics ECG (i.e., in the sense that it selects only one frame per cardiac cycle), but will automatically choose the fraction of the cycle that renders the most stable gated frame set. Stability here is gauged by measuring interframe similarity. Our method operates exclusively on the imagery data and does not require ECG or any form of image segmentation or other high-level image analysis. To validate our algorithm, we compare its behavior versus true ECG gating.     

Multiscale-patient-specific artery and atherogenesis models

In this work, we present a platform for the development of multiscale patient-specific artery and atherogenesis models. The platform, called ARTool, integrates technologies of 3-D image reconstruction from various image modalities, blood flow and biological models of mass transfer, plaque characterization, and plaque growth. Patient images are acquired for the development of the 3-D model of the patient specific arteries. Then, blood flow is modeled within the arterial models for the calculation of the wall shear stress distribution (WSS). WSS is combined with other patient-specific parameters for the development of the plaque progression models. Real-time simulation can be performed for same cases in grid environment. The platform is evaluated using both animal and human data.