Cystatin C Is Associated with the Extent and Characteristics of Coronary Atherosclerosis in Patients with Preserved Renal Function.

Cystatin C (CysC), an endogenous inhibitor of cysteine proteases and a sensitive and accurate marker of renal function, is associated with the severity of coronary atherosclerosis assessed by angiography and future cardiovascular events according to previous studies. We aimed to evaluate the association between CysC levels and coronary plaque volume, composition and phenotype assessed by intravascular ultrasound and intravascular ultrasound-derived virtual histology in patients with preserved renal function. Forty-four patients with angiographically documented coronary artery disease and complete intravascular imaging were included in the study. Patients were categorized into tertiles by CysC levels. Subjects in the high CysC tertile had significantly higher mean plaque burden (48.0 % ± 6.9 vs. 42.8 % ± 7.4, P = 0.029), lower mean lumen area (8.1 mm2 ± 1.7 vs. 9.9 mm2 ± 3.1, P = 0.044) and a higher number of 5-mm vessel segments with minimum lumen area < 4 mm2 (17.9 ± 18.9 vs. 6.8 ± 11.7, P = 0.021) compared to patients in the lower tertiles. In addition, CysC levels demonstrated significant positive correlation with the mean plaque burden (r = 0.35, P = 0.021). Neither relative, nor absolute plaque components differed significantly according to CysC tertiles. The Liverpool Active Plaque Score was significantly higher in the high CysC tertile patients (0.91 ± 1.0 vs. 0.18 ± 0.92, P = 0.02). In conclusion, our study demonstrated a significant association of increased CysC levels with more advanced coronary artery disease and higher risk plaque phenotype in patients with preserved renal function.

Prediction of coronary vessel involvement on the basis of atherosclerosis risk factor analysis.

The prediction of coronary vessel involvement by means of noninvasive tests is one of the fundamental objectives of preventive cardiology. This review describes the current possibilities of coronary vessel involvement prediction by means of ultrasonographic examination of carotid arteries, analysis of polymorphisms in the genes encoding enzymes responsible for production of nitric oxide and carbon monoxide and assessment of levels of certain proinflammatory cytokines. In the presented work these noninvasive markers are correlated with the extent of coronary vessel involvement as assessed by coronary angiography, intravascular ultrasound and virtual histology (Fig. 5, Ref. 40).

Genetic variants in haem oxygenase-1 and endothelial nitric oxide synthase influence the extent and evolution of coronary artery atherosclerosis.

The genetic basis for atherosclerosis development and progression is poorly characterized. We aimed to assess the relationship between endothelial nitric oxide synthase (ENOS) 894 G/T, haem oxygenase-1 (HO1) dinucleotide-length promoter polymorphisms and coronary artery atherosclerotic invol vement and its changes during statin therapy. Coronary angiography, intravascular ultrasound (IVUS), IVUS-derived virtual histology (VH) and genetic polymorphism analysis were performed at study entry. Patients were randomized 1:1 to standard or aggressive hypolipidaemic treatment, and a follow-up evaluation was performed after twelve months. Plaque magnitude was significantly higher in carriers of HO1 risk variants when compared with carriers of the protective variants (< 25 GT repeats). Similarly, the total coronary atherosclerotic burden was significantly greater in HO1 risk variant carriers than in HO1 protective variant carriers. Both parameters did not differ with respect to the ENOS genotype. A higher prevalence of thin-cap fibroatheroma (TCFA) in HO1 risk variant carriers was observed, compared with the HO1 protective variant carriers. The prevalence of TCFA was not influenced by the ENOS genotype. Baseline plaque composition did not differ significantly with respect to both polymorphisms. Significant interactions between plaque composition changes and ENOS and HO1 genotypes were observed during statin treatment. In conclusion, the protective HO1 promoter polymorphism correlates with a lower coronary artery plaque burden, whereas the protective ENOS 894 G/T polymorphism seems to favourably influence changes of coronary artery plaque composition during statin therapy, but has no significant correlation to the magnitude of coronary atherosclerosis.

A new osteophyte segmentation algorithm using partial shape model and its applications to rabbit femur anterior cruciate ligament transection via micro-CT imaging.

Osteophyte is an additional bony growth on a normal bone surface limiting or stopping motion at a deteriorating joint. Detection and quantification of osteophytes from CT images is helpful in assessing disease status as well as treatment and surgery planning. However, it is difficult to distinguish between osteophytes and healthy bones using simple thresholding or edge/texture features due to the similarity of their material composition. In this paper, we present a new method primarily based active shape model (ASM) to solve this problem and evaluate its application to anterior cruciate ligament transection (ACLT) rabbit femur model via CT imaging. The common idea behind most ASM based segmentation methods is to first build a parametric shape model from a training dataset and apply the model to find a shape instance in a target image. A common challenge with such approaches is that a diseased bone shape is significantly altered at regions with osteophyte deposition misguiding an ASM method and eventually leading to suboptimum segmentations. This difficulty is overcome using a new partial ASM method that uses bone shape over healthy regions and extrapolates it over the diseased region according to the underlying shape model. Finally, osteophytes are segmented by subtracting partial-ASM derived shape from the overall diseased shape. Also, a new semi-automatic method is presented in this paper for efficiently building a 3D shape model for an anatomic region using manual reference of a few anatomically defined fiducial landmarks that are highly reproducible on individuals. Accuracy of the method has been examined on simulated phantoms while reproducibility and sensitivity have been evaluated on CT images of 2-, 4- and 8-week post-ACLT and sham-treated rabbit femurs. Experimental results have shown that the method is highly accurate ( R2 = 0.99), reproducible (ICC = 0.97), and sensitive in detecting disease progression (p-values: 0.065,0.001 and < 0.001 for 2- vs. 4, 4- vs. 8- and 2- vs. 8-weeks, respectively).

Comparison of left anterior descending coronary artery hemodynamics before and after angioplasty.

Coronary artery disease (CAD) is characterized by the progression of atherosclerosis, a complex pathological process involving the initiation, deposition, development, and breakdown of the plaque. The blood flow mechanics in arteries play a critical role in the targeted locations and progression of atherosclerotic plaque. In coronary arteries with motion during the cardiac contraction and relaxation, the hemodynamic flow field is substantially different from the other arterial sites with predilection of atherosclerosis. In this study, our efforts focused on the effects of arterial motion and local geometry on the hemodynamics of a left anterior descending (LAD) coronary artery before and after clinical intervention to treat the disease. Three-dimensional (3D) arterial segments were reconstructed at 10 phases of the cardiac cycle for both pre- and postintervention based on the fusion of intravascular ultrasound (IVUS) and biplane angiographic images. An arbitrary Lagrangian-Eulerian formulation was used for the computational fluid dynamic analysis. The measured arterial translation was observed to be larger during systole after intervention and more out-of-plane motion was observed before intervention, indicating substantial alterations in the cardiac contraction after angioplasty. The time averaged axial wall shear stress ranged from -0.2 to 9.5 Pa before intervention compared to -0.02 to 3.53 Pa after intervention. Substantial oscillatory shear stress was present in the preintervention flow dynamics compared to that in the postintervention case.

Evaluation of a new method for automated detection of left ventricular boundaries in time series of magnetic resonance images using an Active Appearance Motion Model.

The purpose of this study was the evaluation of a computer algorithm for the automated detection of endocardial and epicardial boundaries of the left ventricle in time series of short-axis magnetic resonance images based on an Active Appearance Motion Model (AAMM). In 20 short-axis MR examinations, manual contours were defined in multiple temporal frames (from end-diastole to end-systole) in multiple slices from base to apex. Using a leave-one-out procedure, the image data and contours were used to build 20 different AAMMs giving a statistical description of the ventricular shape, gray value appearance, and cardiac motion patterns in the training set. Automated contour detection was performed by iteratively deforming the AAMM within statistically allowed limits until an optimal match was found between the deformed AAMM and the underlying image data of the left-out subject. Global ventricular function results derived from automatically detected contours were compared with results obtained from manually traced boundaries. The AAMM contour detection method was successful in 17 of 20 studies. The three failures were excluded from further statistical analysis. Automated contour detection resulted in small, but statistically nonsignificant, underestimations of ventricular volumes and mass: differences for end-diastolic volume were 0.3%+/-12.0%, for end-systolic volume 2.0%+/-23.4% and for left ventricular myocardial mass 0.73%+/-14.9% (mean+/-SD). An excellent agreement was observed in the ejection fraction: difference of 0.1%+/-6.7%. In conclusion, the presented fully automated contour detection method provides assessment of quantitative global function that is comparable to manual analysis.

Fluid dynamic analysis in a human left anterior descending coronary artery with arterial motion.

A computational fluid dynamic (CFD) analysis is pre sented to describe local flow dynamics in both 3-D spatial and 4-D spatial and temporal domains from reconstructions of intravascular ultrasound (IVUS) and bi-plane angiographic fusion images. A left anterior descending (LAD) coronary artery segment geometry was accurately reconstructed and subsequently its motion was incorporated into the CFD model. The results indicate that the incorporation of motion had appreciable effects on blood flow patterns. The velocity profiles in the region of a stenosis and the circumferential distribution of the axial wall shear stress (WSS) patterns in the vessel are altered with the wall motion introduced in the simulation. The time-averaged axial WSS between simulations of steady flow and unsteady flow without arterial motion were comparable (-0.3 to 13.7 Pa in unsteady flow versus -0.2 to 10.1 Pa in steady flow) while the magnitudes decreased when motion was introduced (0.3-4.5 Pa). The arterial wall motion affects the time-mean WSS and the oscillatory shear index in the coronary vessel fluid dynamics and may provide more realistic predictions on the progression of atherosclerotic disease.

Multi-View Active Appearance Models: application to X-ray LV angiography and cardiac MRI.

This paper describes a Multi-View Active Appearance Model (AAM) for coherent segmentation of multiple cardiac views. Cootes' AAM framework was adapted by considering shapes and intensities from multiple views, while eliminating trivial difference in object pose in different views. This way, the coherence in organ shape and intensities between different views is modeled, and utilized during image search. The method is validated in two substantially different and novel applications: segmentation of combined end-diastolic and end-systolic left ventricular X-ray angiograms, and simultaneous segmentation of a combination of four chamber, two chamber and short-axis cardiac MR views.

Highly automated segmentation of arterial and venous trees from three-dimensional magnetic resonance angiography (MRA).

With the development of new magnetic resonance (MR) contrast agents that have longer persistence in the blood, contrast-enhanced magnetic resonance angiography (MRA) facilitates non-invasive imaging of the cardiovascular system at high resolution in large anatomic volumes. These high resolution 'steady state' images have simultaneous enhancement of both the artery and vein blood pools. Consequently, separation of arteries and veins is an emerging challenge in MRA analysis. Because of the complexity of the vascular structure, manual approaches to cardiovascular tree analysis are impractical. A novel, highly-automated low extremity vessel segmentation and display methodology is reported that consists of five main steps: (1) Binary mask generation, (2) tree-structure generation, (3) optimal vessel path calculation, (4) vessel segment labeling, and (5) conflict resolution. The method's performance was tested in computer phantoms and in in vivo data sets. In the computer-generated phantoms, vessel volume errors ranged from 1.0 to 8.8%. In the in vivo data, the labeling errors ranged from 0.1 to 15.5%. The method provided high quality results in individual data sets and demonstrated segmentation robustness.

Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images.

A fully automated approach to segmentation of the left and right cardiac ventricles from magnetic resonance (MR) images is reported. A novel multistage hybrid appearance model methodology is presented in which a hybrid active shape model/active appearance model (AAM) stage helps avoid local minima of the matching function. This yields an overall more favorable matching result. An automated initialization method is introduced making the approach fully automated. Our method was trained in a set of 102 MR images and tested in a separate set of 60 images. In all testing cases, the matching resulted in a visually plausible and accurate mapping of the model to the image data. Average signed border positioning errors did not exceed 0.3 mm in any of the three determined contours-left-ventricular (LV) epicardium, LV and right-ventricular (RV) endocardium. The area measurements derived from the three contours correlated well with the independent standard (r = 0.96, 0.96, 0.90), with slopes and intercepts of the regression lines close to one and zero, respectively. Testing the reproducibility of the method demonstrated an unbiased performance with small range of error as assessed via Bland-Altman statistic. In direct border positioning error comparison, the multistage method significantly outperformed the conventional AAM (p < 0.001). The developed method promises to facilitate fully automated quantitative analysis of LV and RV morphology and function in clinical setting.

Automated analysis of Doppler ultrasound velocity flow diagrams.

A highly automated method for the identification and quantization of maximum blood velocity curves from Doppler ultrasound flow diagrams is presented. The method uses an image processing scheme to analyze video-recorded image sequences of flow diagrams. The sequences are acquired, a sequence of images relating to chronological cardiac cycles is extracted, and a maximum blood velocity envelope is determined and quantified. The results are verified against hand-traced reference curves. Excellent correlation of r = 0.99 is achieved.

Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples.

This paper provides methodology for fully automated model-based image segmentation. All information necessary to perform image segmentation is automatically derived from a training set that is presented in a form of segmentation examples. The training set is used to construct two models representing the objects--shape model and border appearance model. A two-step approach to image segmentation is reported. In the first step, an approximate location of the object of interest is determined. In the second step, accurate border segmentation is performed. The shape-variant Hough transform method was developed that provides robust object localization automatically. It finds objects of arbitrary shape, rotation, or scaling and can handle object variability. The border appearance model was developed to automatically design cost functions that can be used in the segmentation criteria of edge-based segmentation methods. Our method was tested in five different segmentation tasks that included 489 objects to be segmented. The final segmentation was compared to manually defined borders with good results [rms errors in pixels: 1.2 (cerebellum), 1.1 (corpus callosum), 1.5 (vertebrae), 1.4 (epicardial), and 1.6 (endocardial) borders]. Two major problems of the state-of-the-art edge-based image segmentation algorithms were addressed: strong dependency on a close-to-target initialization, and necessity for manual redesign of segmentation criteria whenever new segmentation problem is encountered.

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.

Geometrically correct 3-D reconstruction of intravascular ultrasound images by fusion with biplane angiography--methods and validation.

In the rapidly evolving field of intravascular ultrasound (IVUS), the assessment of vessel morphology still lacks a geometrically correct three-dimensional (3-D) reconstruction. The IVUS frames are usually stacked up to form a straight vessel, neglecting curvature and the axial twisting of the catheter during the pullback. Our method combines the information about vessel cross-sections obtained from IVUS with the information about the vessel geometry derived from biplane angiography. First, the catheter path is reconstructed from its biplane projections, resulting in a spatial model. The locations of the IVUS frames are determined and their orientations relative to each other are calculated using a discrete approximation of the Frenet-Serret formulas known from differential geometry. The absolute orientation of the frame set is established, utilizing the imaging catheter itself as an artificial landmark. The IVUS images are segmented, using our previously developed algorithm. The fusion approach has been extensively validated in computer simulations, phantoms, and cadaveric pig hearts.

Computer recognition of regional lung disease patterns.

We have developed an objective, reproducible, and automated means for the regional evaluation of the pulmonary parenchyma from computed tomography (CT) scans. This method, known as the Adaptive Multiple Feature Method (AMFM) assesses as many as 22 independent texture features in order to classify a tissue pattern. In this study, the six tissue patterns characterized were: honeycombing, ground glass, bronchovascular, nodular, emphysemalike, and normal. The lung slices were evaluated regionally using 31 x 31 pixel regions of interest. In each region of interest, an optimal subset of texture features was evaluated to determine which of the six patterns the region could be characterized as. The computer output was validated against experienced observers in three settings. In the first two readings, when the observers were blinded to the primary diagnosis of the subject, the average computer versus observer agreement was 44.4 +/- 8.7% and 47.3 +/- 9.0%, respectively. The average interobserver agreement for the same two readings were 48.8 +/- 9.1% and 52.2 +/- 10.0%, respectively. In the third reading, when the observers were provided the primary diagnosis, the average computer versus observer agreement was 51.7 +/- 2.9% where as the average interobserver agreement was 53.9 +/- 6.2%. The kappa statistic of agreement between the regions, for which the majority of the observers agreed on a pattern type, versus the computer was found to be 0.62. For regional tissue characterization, the AMFM is 100% reproducible and performs as well as experienced human observers who have been told the patient diagnosis.

Interstitial lung disease: A quantitative study using the adaptive multiple feature method.

We have previously described an adaptive multiple feature method (AMFM) for the objective assessment of global and regional changes in pulmonary parenchyma to detect emphysema. This computerized method uses a combination of statistical and fractal texture features for characterization of lung tissues based upon high resolution computed tomography (HRCT) scans. This present study was a substantial extension of the AMFM to simultaneously discriminate between multiple pulmonary disease processes. Normal subjects and those with emphysema, idiopathic pulmonary fibrosis (IPF), or sarcoidosis were studied. The AMFM was compared with two currently utilized computer-based methods: mean lung density (MLD) and the histogram analysis (HIST). Globally, when comparing two-subject groups the AMFM overall accuracy was 2 to 18% better than the overall accuracy of MLD and as much as 36% better than the accuracy of the HIST methods. In three-subject group discrimination tasks, the AMFM performed 7 to 27% better than the MLD and 4 to 36% better than the HIST methods. Finally, in discriminating all four subject groups at a time, the AMFM overall accuracy was 81%, which was 21% better than the MLD and 25% better than the HIST method. In most three-subject group comparisons and in the four-subject group comparison, the AMFM was significantly (p < 0.01) better than the MLD and HIST methods. Next, the AMFM was applied to local discrimination between normal and each disease group individually. The normal versus emphysema, normal versus IPF, and normal versus sarcoidosis samples were discriminated with an accuracy of 95, 86, and 77%, respectively. The AMFM is an objective quantitative method that can be adapted for successful discrimination of multiple parenchymal lung diseases.

Biplane X-ray angiograms, intravascular ultrasound, and 3D visualization of coronary vessels.

The technology for determination of the 3D vascular tree and quantitative characterization of the vessel lumen and vessel wall has become available. With this technology, cardiologists will no longer rely primarily on visual inspection of coronary angiograms but use sophisticated modeling techniques combining images from various modalities for the evaluation of coronary artery disease and the effects of treatment. Techniques have been developed which allow the calculation of the imaging geometry and the 3D position of the vessel centerlines of the vascular tree from biplane views without a calibration object, i.e., from the images themselves, removing the awkwardness of moving the patient to obtain 3D information. With the geometry and positional information, techniques for reconstructing the vessel lumen can now be applied that provide more accurate estimates of the area and shape of the vessel lumen. In conjunction with these developments, techniques have been developed for combining information from intravascular ultrasound images with the information obtained from angiography. The combination of these technologies will yield a more comprehensive characterization and understanding of coronary artery disease and should lead to improved and perhaps less invasive patient care.

Segmentation of intrathoracic airway trees: a fuzzy logic approach.

Three-dimensional (3-D) analysis of airway trees extracted from computed tomography (CT) image data can provide objective information about lung structure and function. However, manual analysis of 3-D lung CT images is tedious, time consuming and, thus, impractical for routine clinical care. We have previously reported an automated rule-based method for extraction of airway trees from 3-D CT images using a priori knowledge about airway-tree anatomy. Although the method's sensitivity was quite good, its specificity suffered from a large number of falsely detected airways. We present a new approach to airway-tree detection based on fuzzy logic that increases the method's specificity without compromising its sensitivity. The method was validated in 32 CT image slices randomly selected from five volumetric canine electron-beam CT data sets. The fuzzy-logic method significantly outperformed the previously reported rule-based method (p < 0.002).

Volume-preserving smoothing of three-dimensional surfaces: application to intravascular ultrasound.

A volume-preserving three-dimensional smoothing approach is described that can be directly applied to 3D medical image data consisting of sets of 2D image slices, e.g., segmented intravascular ultrasound image sequences. Two local smoothing filters ℱ and 𝒢 were designed according to different smoothing goals and their performance was compared. Filtering with the ℱ filter of a relatively large frequency window keeps the important local characteristics of the object and results in little shrinkage while removing noise. Filtering with the Gaussian filter G that has an added volume compensation step results in no global shrinkage and may be used for multiscale filtering. The two filters can be easily extended to n-dimensional filtering.

Semiautomated segmentation of ovarian follicular ultrasound images using a knowledge-based algorithm.

The application of a knowledge-based segmentation method to the problem of automatically detecting the outer follicle wall boundary in ultrasonographic images of ovaries is presented. A combination of computer detection and interactive adjustment was used to define an approximate inner follicle-wall boundary, which was then used by the computer algorithm as a priori knowledge to automatically find the outer follicle-wall border. The segmentation algorithm was tested on ultrasonographic images of women's ovaries that were imaged in vivo. The semiautomatic segmentations were compared to segmentations by an expert human observer in terms of border placement differences and in terms of quantitative parameters relevant to the physiologic status of the follicles. These physiological parameters include total and specific signal intensity from the follicle and from the follicle wall. The computer-detected outer follicle wall boundaries correlated well with the human observer-defined wall boundaries, in terms of enclosed follicle area, specific and total follicle signal, enclosed wall area, and specific and total wall signal. The actual border placement differences were also small, with a maximum placement difference of 1.47 +/- 0.83 mm and a root mean square (r.m.s.) placement difference of 0.59 +/- 0.28 mm.