Cardiovascular Informatics

Transcription

1 This is page 1 Printer: Opaque this Cardiovascular Informatics I.A. Kakadiaris, U. Kurkure, A.N. Bandekar, S. O Malley and M. Naghavi 1 ABSTRACT As cardiovascular imaging technologies advance, large amounts of imaging data are being produced which are not being mined sufficiently by current diagnostic tools, particularly for early detection and diagnosis of asymptomatic cardiovascular disease. We aim to develop a computational framework to mine cardiac imaging data and provide quantitative measures for developing a new risk assessment method. In this chapter, we present novel methods to quantify pericardial fat in non-contrast cardiac computed tomography images automatically, and to detect and quantify neovascularization in the coronary vessels using intra-vascular ultrasound imaging. 1 Introduction Cardiovascular disease has long been the leading cause of death in developed countries, and it is rapidly becoming the number one killer in developing countries [3]. In 2006, it is estimated that more than 19 million people worldwide experienced a life-threatening heart attack. In the US alone, 1.4 million people suffer from a heart attack annually. Of the 140 million Americans between the ages of 35-44, 17.4% of males (24.36 million) and 13.6% of females (19.04 million) have coronary heart disease. Approximately 50% of heart attack related deaths occur in people with no prior symptoms [6]. Hence, sudden heart attacks remain the number one cause of death in the US. Unpredicted heart attacks account for the majority of the $280 billion burden of cardiovascular diseases. Coronary artery disease occurs as a result of atherosclerosis, a condition in which a fatty substance called plaque builds up on the walls of the arteries. If these plaques rupture, blood clots form that obstruct the flow of blood to the heart and may cause a heart attack, which can often be fatal. Some plaques present a particularly high risk of rupture and a subsequent heart attack. All types of atherosclerotic plaques with a high likelihood of thrombotic complications and rapid progression are recognized as vulnerable plaques. The field of cardiology has witnessed a major paradigm shift in its determination of patients at risk of coronary artery disease. In the 1 I.A. Kakadiaris, U. Kurkure, A.N. Bandekar, and S. O Malley are with CBL, University of Houston, Houston, TX. M. Naghavi is with AEHA, Houston, TX.

2 2 I.A. Kakadiaris, U. Kurkure, A.N. Bandekar, S. O Malley and M. Naghavi past, increasing fat deposition and gradual inward luminal narrowing of coronary arteries were thought to be the culprits in heart attacks. Today, cardiovascular specialists know that heart attacks are caused by inflammation of the coronary arteries and thrombotic complications of vulnerable plaques. As a result, the discovery of vulnerable plaque has recently evolved into the definition of vulnerable patient. A vulnerable patient is defined as a person with more than 10% likelihood of having a heart attack in the next 12 months. Over 45 world leaders in cardiology have collectively introduced the field of vulnerable patient detection as the new era in preventive cardiology [7, 8]. Until a few years ago many cardiovascular specialists did not have many of the novel diagnostic tools that are becoming increasingly available today. These emerging diagnostic tests include: 1) novel genetic and serum biomarkers such as CRP and inflammatory markers; 2) noninvasive imaging tests such as Computed Tomography (CT), and CT Angiography (CTA), and ultrasound-based intima-media thickness measurement; and 3) new interventional catheter-based diagnostic tools (e.g., intravascular ultrasound (IVUS)). All of these provide unprecedented opportunities for early detection and risk stratification of subjects at risk of heart attack. A nonprofit initiative pioneered by Association for Eradication of Heart Attacks (AEHA), Screening for Heart Attack Prevention and Education (SHAPE), presents a practice guideline for doctors to implement public screening of at-risk populations, calling for men 45 and older and women 55 and older to undergo a comprehensive vascular health assessment [6]. It comprises a pyramid of tests with serum tests and Framingham Risk Score assessment at the bottom of the pyramid, non-invasive imaging such as CT and CTA in the middle, and IVUS imaging at the top. If a patient is found to be at risk with the tests at one level, then s/he is referred to tests in the next level to allow better localization. Considering the large amounts of data that the SHAPE program will produce, there is an urgent need for computational tools to assist in screening for the conditions that underlie sudden cardiac events. Existing cardiovascular risk scoring methods do not take into account all the wealth of information that is available in imaging data. The reason is two-fold: 1) there is a lack of automatic techniques to mine the data for the required information, and 2) validation in large epidemiological studies is needed to determine which type of information will offer additive predictive value. Our long term vision is to contribute to the development of quantitative methods to assess cumulative risk of vulnerable patients by developing new techniques to mine additional information from their imaging data. In this chapter, we will focus on presenting methods for the analysis of CT and IVUS data. On one hand, CT can provide information to assess abdominal and pericardial fat. Recent evidence indicates that pericardial fat may be a significant cardiovascular risk factor [16]. Although pericardial fat is routinely imaged during CT for coronary calcium scoring, it is currently ignored in

3 1. Cardiovascular Informatics 3 the analysis of CT images. The primary reason for this is the absence of a tool capable of automatic quantification of pericardial fat. Recent studies on pericardial fat imaging were limited to manually outlined regions-ofinterest and preset fat attenuation thresholds, which are subject to interobserver and inter-scan variability. In general, there is an increased demand for automated, robust, and accurate quantitative measures of these tissues in the heart region. An accurate segmentation allows for quantitative and morphological analysis. Thus, the development of fully automated methods which will provide unbiased and consistent results is highly desirable. On the other hand, IVUS, which is a catheter-based ultrasound technology, can provide real-time video of the interior of a blood vessel at video frame rates (10 to 30 frames/sec). It is used routinely for the detailed assessment of vessel morphology and atherosclerotic plaque, often as a guide for interventional procedures (e.g., angioplasty) where alternative non-invasive imaging techniques such as X-ray angiography are insufficient. Recent years have witnessed a plethora of advancements [11, 12] in IVUS technology in an effort to extract additional diagnostic information from these sensors. A number of techniques have been developed to allow increased performance of IVUS contrast imaging, in which an echogenic solution of microbubbles is incorporated into the bloodstream as a tracer of perfusion. Unfortunately, the newest methods for contrast imaging remain experimental and tend to require non-standard IVUS hardware [4] (e.g., harmonic imaging catheters), non-standard contrast agents, or both. Specifically, in this chapter, we describe a unified knowledge-based medical image segmentation framework and a multi-class, multi-feature fuzzy connectedness-based tissue classification and segmentation method, which were used for automated detection and quantification of pericardial fat in non-contrast cardiac CT images. We also present a novel imaging protocol for contrast imaging in IVUS, employing stationary-catheter sequences, along with the computational tools necessary for frame-gating, contourtracking and processing the resulting data, to image the extra-luminal perfusion due to blood flow through the vasa vasorum (VV) [5, 17, 18]. This IVUS contrast imaging is accomplished using commercially-available IVUS systems and off-the-shelf microbubble contrast agents and has enabled quantification of VV presence in vivo for the first time. The rest of the chapter is organized as follows. In Section 2, we present methods for CT data analysis. In Section 3, we present methods for IVUS data analysis. In Section 4, we present the validation results from our methods. In Section 5, we discuss the advantages and limitations of our work. 2 CT Data Analysis In this section, we describe a unified knowledge-based medical image segmentation framework [2]. We present a knowledge-based atlas which includes spatial information from a probabilistic atlas, texture-based features relationships between organs in the atlas, and image analysis methods for

4 4 I.A. Kakadiaris, U. Kurkure, A.N. Bandekar, S. O Malley and M. Naghavi segmentation of specific tissues or organs in the atlas. Our framework consists of a training phase and a deployment phase. In the training phase, we construct a probabilistic atlas of the region of interest (ROI) and learn various parameters for each component in the framework. In the deployment phase, we use the probabilistic atlas for initialization of the ROI and employ the learned parameters for further refinement of the ROI using the image analysis methods predefined for the segmentation of a specific organ or tissue in the ROI. Such a unified knowledge-based framework can be generalized for the segmentation of various human body regions such as the head, neck, pelvis, and other anatomical structures. We also present a multi-class multi-feature region classification and segmentation method based on fuzzy-connectedness formulation for fat tissue detection. By relaxing the connectedness criterion, this method can be used for fat tissue classification [1]. Knowledge-based atlas: We consider the data D to be a bounded subset of R 3. We assume that we have a priori knowledge of the N 1 organs present in the region D. Our knowledge-based atlas K used to describe the ROI D consists of four elements, namely, a probabilistic atlas (P), texturebased features (T ), relationships (R), and specific segmentation methods (M) for organs in the region D, K(D) ={P, T, R, M}. We construct a probabilistic atlas P for our data D, which is a map that assigns to each voxel a set of probabilities to belong to each of the organs, P(D) =(p 0,p 1,p 2,..., p N ), where p i is the probability for the voxel to belong to organ i, (1 i N). All the organs other than those that are manually delineated are assigned the probability p 0 where p 0 =1 Σ N k=1 p k. The probabilistic atlas is constructed in the training phase of the framework. We initialize the organs present in the atlas during deployment using registration techniques described in [15]. This helps us to gain insight on the spatial information of different organs and variations with respect to one another, and also guides automated segmentation procedures. Every organ will present different texture-based features depending on the image modality and the tissue type of the organ. We denote the set of texture-based features by, T = {T 1,T 2,..., T N }, where T i =[f i 1,f i 2,..., f i k ] is set of optimal texture-based features selected in the training phase for discrimination for organ i, (1 i N) and k is the number of features for organ i. The optimal texture-based features are selected in the training phase of our framework. Texture-based features are used by the image analysis methods to segment the organs and refine the initialization by the probabilistic atlas. Relationships are a set of rules which describe relationships between two organs in the region. Based on prior anatomical knowledge about the organs, we consider two types of relationships: hierarchical (e.g., child-parent) and spatial (e.g., posterior-anterior). We denote the set of relationships by R = {R r,t r, t organs}, where R r,t {posterior, anterior, right, left, child, parent}, r is the reference organ and t is the target organ. For example,

5 1. Cardiovascular Informatics 5 R i,j = child means that, the reference organ i (e.g., ventricle) is the child of the target organ j (e.g., heart); while R i,j = right implies that the reference organ i (e.g., right lung) is to the right of the target organ j (e.g., heart). Image analysis methods are specific methods set during the training phase for a specific organ. We denote the set of all methods by, M = {M 1,M 2,..., M K }. Due to high anatomic variations and the large amount of structural information found in medical images, global information based segmentation methods yield inadequate results in region extraction. We use the knowledge-based atlas to guide the automatic segmentation process and then use specific image analysis methods for segmentation of anatomical structures. We examine the performance of these methods in the training phase and accordingly select methods with higher accuracy and true positive rate as well as lower false positive rate. One of the segmentation methods used is the multi-class multi-feature fuzzy connectedness method, which is described in the following section. Multi-class, multi-feature fuzzy connectedness: The anatomical objects in medical data are characterized by certain intensity level and intensity homogeneity features. Also, additional features can be computed to characterize certain properties of a tissue class. Such features can be used to distinguish between different types of tissue classes. Our multiclass, multi-feature fuzzy connectedness method is able to take advantage of multiple features to distinguish between multiple classes for segmentation and classification. We define three kinds of fuzzy affinities: local fuzzy spel affinity, global object affinity, and global class affinity. The local fuzzy spel affinity (µ κ ) consists of three components: 1. the object feature intensity component (µ φ ), 2. the intensity homogeneity component (µ ψ ), and 3. the texture feature component (µ ϕ ). The similarity of the pixels feature vectors is computed using 2 the Mahalanobis metric: m d(c d) =(x (c d) x (c d) ) S 1 (c d) (x (c d) x (c d) ), where x (c d), x (c d), S (c d) are the feature vector, the mean feature vector, and the covariance matrix in the direction from c to d, respectively. The bias in intensity in a specific direction is accounted for by allowing different levels and signs of intensity homogeneities in different directions of adjacency [13]. Thus, this formulation accounts for different levels of the increase or decrease in intensity values in the horizontal (left, right) or vertical (up, down) directions. The advantage of using the Mahalanobis metric is that it weighs the differences in various feature dimensions by the range of variability in the direction of the feature dimension. These distances are computed in units of standard deviation from the mean. This allows us to assign a statistical probability to the measurement. The local 1 fuzzy spel affinity is computed as: µ κ (c, d) = 1+m d(c d) in order to ensure that µ κ (c, d) Z 2 [0, 1] and it is reflexive and symmetric, where Z 2 is a set of all pixels of a two-dimensional Euclidean space.

6 6 I.A. Kakadiaris, U. Kurkure, A.N. Bandekar, S. O Malley and M. Naghavi Fuzzy connectedness captures the global hanging-togetherness of pixels by using the local affinity relation and by considering all possible paths between two, not necessarily nearby, pixels in the image. It considers the strengths of all possible paths between given two pixels, where the strength of a particular path is the weakest affinity between the successive pairs of pixels along the path. Thus, the strongest connectedness path between the given two pixels specifies the degree of global hanging togetherness between the given two pixels. Global object affinity is the largest of the weakest affinities between the successive pairs of pixels along the path p cd of all possible paths P cd from c to d and is given by µ K (c, d) = max pcd P cd {min 1 i m [µ κ (c (i),c (i+1) )]}. In our framework, the global object affinity and local pixel affinity are assigned only if the global class affinity (or discrepancy measure) of c and d belonging to the neighboring objects classes is more (or less) than a predefined value, γ (note that the affinity value has an inverse relationship with the Mahalanobis distance metric in our formulation). The minimum discrepancy measure J(c, d) = min 1 i b m d (c, d), where b is the number of neighboring classes of the target object, gives the maximum membership value of a pixel pair belonging to a certain class. If J(c, d) <γ, and the class to which the pixel pair belongs is not the target object class, then the local pixel affinity µ κ(c,d) is set to zero, else its local pixel affinity is computed as described earlier. Since pericardial fat tissue appears as disjoint sets of pixels distributed all over in the thoracic cavity, we relax the spatial connectedness constraint in our formulation to allow scattered tissue classification (Fig. 1). Our segmentation algorithm uses dynamic statistics of fat tissue for automatic segmentation of the fat tissue. We estimate the statistics of fat tissue by using a sample region around a seed point, hence the selection of the seed point is very critical. We obviate the need for manual seed selection by automatic seed initialization using relationships defined in the training phase. Thus, instead of applying a space-invariant global threshold value, our method adapts the threshold value locally in the feature space. In addition to fat segmentation in CT, this method has also been used for left ventricular segmentation in MRI [14]. 3 IVUS Data Analysis In this section, we introduce a number of techniques for IVUS image analysis [9, 10] which enable the use of difference imaging to detect those changes which occur in the IVUS imagery due to the perfusion of an intravascularlyinjected contrast agent into the plaque and vessel wall. To determine if a particular physical region of the frame experiences an increase in echogenicity due to contrast perfusion into the wall, it is necessary to compare the appearance of that region under IVUS before and after the introduction of contrast. Since multiple sources of motion are present, we follow three steps to detect perfusion: motion compensation, image subtraction and deriving

7 1. Cardiovascular Informatics 7 statistics from the resulting difference images (Fig. 2(a)). The goal of two-step motion compensation (frame gating and contourtracking) is to provide pixelwise correspondence between an ROI (e.g., the plaque) in each frame. Unlike previous efforts utilizing ECG signals, we perform an appearance-based grouping of frames. By formulating the problem in terms of multidimensional scaling (MDS), a number of other useful operations may be performed. The MDS transform places points defined only by inter-point proximities into a metric space such that the proximities are preserved with minimal loss. In our context, this allows the creation of a frame-similarity space which may be employed as a concise visual and numerical summary of an entire frame sequence. Clustering this space allows sets of frames with various similarity properties to be extracted efficiently. We begin by taking our n-frame sequence and creating a square matrix D in which each entry d i,j represents the dissimilarity between frames i and j. As normalized cross-correlation returns values in the range [ 1, +1], we clamp these values to [0, +1] and subtract them from one. This results in a matrix with zero along the diagonal and values on the range [0, 1] everywhere else. Next, we let A be the matrix where a i,j = 1 2 d2 i,j and let C n = I n 1 n 1 n1 T n, where I n is the n n identity matrix and 1 n is the unit vector of length n. Let B = C n AC n. We let λ 1 λ n and v 1,..., v n be the eigenvalues and associated eigenvectors of B, and p be the number of positive eigenvalues. By forming the matrix Y = ( λ 1 v 1,..., ) λ p v p, we obtain a p-dimensional point cloud in which each frame is represented in space by a single point. The distance between any two points i and j in Y approximates the dissimilarity between those frames as represented in D. We use randomly-initialized k-means with multiple runs to converge to a lowest-error clustering [9, 10]. To eliminate residual motion artifacts after frame gating, a more precise contour tracking operation is performed to provide pixel-wise correspondence. Two contours are drawn which define the inner and outer boundaries. Initialization for a single contour comes in the form of a manuallydrawn contour on the first frame. Contours are found for all frames after the first by pairwise matching. Our method follows a two-step approach to complete the process: a rigid alignment step followed by an elastic refinement step. In the rigid step, starting with static-image contour x, the following rigid transformations are modeled to match the contour to the moving image: ±x translation, ±y translation, ± rotation, and ± dilation. Then, we proceed using a gradient ascent. In the elastic step, given the contour x, which is itself a rigid transformation of the initial contour x, we deform x elastically in order for it to better conform to the image features associated spatially with x. The output of this elastic matching step is a refined contour, x. We define a contour energy function for any contour, and by manipulating x we seek to maximize the energy function to produce x. Lastly, if information about of the regions on the inside and outside of

8 8 I.A. Kakadiaris, U. Kurkure, A.N. Bandekar, S. O Malley and M. Naghavi the contour is known, it is possible to use histogram statistics to influence the deforming contour. Given tracked contour pairs for each image in our gated IVUS sequence, the region between the contours is resampled from image space into a rectangular region space. Difference imaging is accomplished by subtracting the pre-injection baseline from all region images in the gated sequence. As the same baseline is subtracted from both the pre- and post-injection frames, it is simpler to determine which deviations from the baseline occur as a result of the contrast injection and which are the result of noise unrelated to the presence of contrast. To quantify enhancement in the difference-imaged regions of interest after they have been mapped back to the IVUS image space, we consider the set of pixels in the ROI which are flagged as enhanced. Averaging the grey levels of these enhanced pixels, we obtain the average enhancement per enhanced pixel (AEPEP) statistic. Each frame in our gated sequence will have one associated AEPEP value; due to noise, pre-injection frames will have some positive value but, if enhancement occurs, the post-injection value will be greater. 4 Results We applied our framework to detect and quantify the pericardial fat tissue in 300 non-contrast cardiac CT scans using the multi-class, multi-feature fuzzy connectedness method. We compared the results of our method (Automatic Fat Analysis in Computed Tomography - AFACT) to expert manual segmentations of pericardial fat. The manual delineation of the fat region in the CT images, performed by experienced physicians/radiologists, was used as the gold standard. We evaluated the results of our algorithm by computing accuracy, true positive rate and true negative rate. Specifically, we computed the false negatives, false positives, true negatives, and true positives by computing the number of pixels that were classified as the background and the fat, both correctly and incorrectly. For 300 subjects, the mean accuracy for pericardial fat was 98.13% ± 2.20%. The mean true negative rate was 99.48% ± 2.20%. Finally, the mean true positive rate was 86.63% ± 9.32% [2]. In vasa vasorum imaging, from 35 patients that were screened initially, 19 patients met the study criteria. The data from three patients were not analyzed due to poor image quality of the arterial layers, suboptimal infusion of the microbubbles, and displacement of the catheter. From the 16 patients, ten had unstable angina, four had ST elevation acute myocardial infarction, and two had non-st elevation myocardial infarction. We performed three different types of analysis to evaluate the performance of our method. First, we examined the stability of the sequences before and after gating. Stability is measured as the mean cross-correlation between every frame pair in a sequence; this is performed for both the gated and ungated sequences. A significant increase was observed in mean cross-correlation for gated sequences (from 0.76 ± 0.08 to 0.84 ± 0.06). Second, for three nor-

9 1. Cardiovascular Informatics 9 mal sequences, the luminal border of 100 frames was manually segmented twice by the same observer. One of these sets of segmentations was used as a ground truth against which the other was compared; the root-meansquared distance between corresponding points on these contours were then measured for each frame as a metric of error. All cases manifested the expected difference in error between our algorithm (2.73 ± 0.47, 2.78 ± 0.91, 4.46 ± 0.92 in pixels for cases 1, 2, and 3 respectively) and normal intraobserver error (2.57 ± 0.74, 3.35 ± 1.11, 1.96 ± 0.97 in pixels for cases 1, 2, and 3 respectively). Note that these sequences were ungated, and hence in practice when we employ our gating algorithm as a pre-processing step, we can expect the error of the contour-tracking step to be lower. Last, contrast-enhanced IVUS studies were performed on the recordings from the 16 human patients. We observed an increase in the mean grey level of all examined regions as expressed by AEPEP from pre- to post-injection of microbubbles image. The percent increase of AEPEP in the ROI after the injection of microbubbles was less than 10% in 3 patients and 10 40% in 13 patients [17, 18]. 5 Discussion We have presented a novel method that allows detection and quantification of pericardial fat in CT. Instead of using a fixed threshold, which is the standard method to detect fat in CT, our method is dynamic and datadriven which adapts to the variations in individual scans. In addition to grey-level information, our method also uses additional derived information (e.g., inhomogeneity and texture features to detect pericardial fat). Our method is fully automatic except for the manual selection of top and bottom axial slices containing the heart. To our knowledge, our method is the first to use multiple features for adaptive data-driven classification of pericardial fat in CT. Clinical investigation of our fat quantification method is warranted to evaluate the role of pericardial fat in risk assessment. We also presented a method which, for the first time, enabled (in vivo) imaging of extra-luminal perfusion under IVUS. Current evidence and suggests that this perfusion is related to the presence of VV microvessels which, in turn, are a potential marker for plaque inflammation and consequent vulnerability. The significant changes in the IVUS signal after microbubble passage leave no doubt as to its ability to show contrast enhancement. The primary limitation of our method is the requirement that we image every area of interest twice: once before and once after the injection of contrast. However, this limitation is necessitated by our desire to restrict ourselves to currently-available IVUS hardware and standard contrast agents. As such, contrast-enhanced IVUS presents a promising imaging approach to the assessment of plaque vulnerability. We are currently in the process of validating our results with histological ground truth to obtain an estimate of the sensitivity of our approach in a clinical setting (Fig. 2(b-e)).

10 10 I.A. Kakadiaris, U. Kurkure, A.N. Bandekar, S. O Malley and M. Naghavi 6 Conclusion Our long term goal is to develop a new risk assessment index for an individual s risk of a cardiovascular event. If these scores are to be used as predictors of a future adverse event, they should provide the best representation of subclinical information. This requires optimal use of all available data. Currently, risk assessment tools do not use the wealth of information available in the images. In this chapter, we presented novel methods to mine information from CT and IVUS data to obtain pericardial fat measurements and to image extra-luminal perfusion due to blood flow through the vasa vasorum, respectively. The results we have obtained are encouraging, and show the possibility of these methods being used in clinical settings. Acknowledgment We would like to thank all members of the Ultimate IVUS team for their valuable assistance. This work was supported in part by NSF Grant IIS and an NSF Graduate Research Fellowship (SMO). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. 7 References [1] A. Bandekar, M. Naghavi, and I. Kakadiaris. Automated pericardial fat quantification in CT data. In Proc. Int. Conf. of the IEEE Engineering in Medicine and Biology Society, pages , New York, NY, [2] A.N. Bandekar. A Unified Knowledge-based Segmentation Framework for Medical Images. PhD thesis, University of Houston, Dec [3] R. Cooperand et al. Trends and disparities in coronary heart disease, stroke, and other cardiovascular diseases in the United States: Findings of the National Conference on Cardiovascular Disease Prevention. Circulation, 102(25): , [4] D.E. Goertz et al. Subharmonic contrast intravascular ultrasound for vasa vasorum imaging. Ultrasound Med Biol, 33(12): , December [5] I. Kakadiaris, S. O Malley, M. Vavuranakis, S. Carlier, R. Metcalfe, C. Hartley, E. Falk, and M. Naghavi. Signal processing approaches to risk assessment in coronary artery disease. IEEE Signal Processing Magazine, 23(6):59 62, [6] M. Naghavi et al. From vulnerable plaque to vulnerable patientpart III: Executive summary of the screening for heart attack prevention and education (SHAPE) task force report. The American Journal of Cardiology, 98(2):2 15, July [7] M. Naghavi et al. From vulnerable plaque to vulnerable patient: A call for new definitions and risk assessment strategies: Part I. Circulation, 108(14): , October 2003.

11 1. Cardiovascular Informatics 11 [8] M. Naghavi et al. From vulnerable plaque to vulnerable patient: A call for new definitions and risk assessment strategies: Part II. Circulation, 108(15): , October [9] S. O Malley, S. Carlier, M. Naghavi, and I. Kakadiaris. Image-based frame gating of IVUS pullbacks: A surrogate for ecg. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, pages , Honolulu, Hawaii, [10] S. O Malley, M. Naghavi, and I. Kakadiaris. Image-based frame gating for stationary-catheter IVUS sequences. In Proc. Int. Workshop on Computer Vision for Intravascular and Intracardiac Imaging, pages 14 21, Copenhagen, Denmark, [11] S. O Malley, M. Naghavi, and I. Kakadiaris. One-class acoustic characterization applied to blood detection in ivus. In Proc. Medical Image Computing and Computer-Assisted Intervention, Brisbane, Australia, [12] S. O Malley, M. Vavuranakis, M. Naghavi, and I. Kakadiaris. Intravascular ultrasound-based imaging of vasa vasorum for the detection of vulnerable atherosclerotic plaque. In Proc. Int. Conf. on Medical Image Computing and Computer Assisted Intervention, volume 1, pages , Palm Springs, CA, USA, [13] A. Pednekar and I.A. Kakadiaris. Image segmentation based on fuzzy connectedness using dynamic weights. IEEE Trans. Image Processing, 15(6): , [14] A. Pednekar, U. Kurkure, I. A. Kakadiaris, R. Muthupillai, and S. Flamm. Left ventricular segmentation in MR using hierarchical multi-class multi-feature fuzzy connectedness. In Proc. Medical Image Computing and Computer Assisted Intervention, Rennes, Saint-Malo, France, Springer. [15] D. Rueckert et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. on Medical Imaging, 18: , [16] R. Taguchi et al. Pericardial fat accumulation in men as a risk factor for coronary artery disease. Atherosclerosis, 157(1):203 9, [17] M. Vavuranakis, I. Kakadiaris, S. O Malley, T. Papaioannou, E. Sanidas, S. Carlier, M. Naghavi, and C. Stefanadis. Contrast enhanced intravascular ultrasound for the detection of vulnerable plaques: a combined morphology and activity-based assessment of plaque vulnerability. Expert Review of Cardiovascular Therapy, 5: , [18] M. Vavuranakis, I.A. Kakadiaris, S. M. O Malley, T. G. Papaioannou, E. A. Sanidas, M. Naghavi, S. Carlier, D. Tousoulis, and C. Stefanadis. A new method for assessment of plaque vulnerability based on vasa vasorum imaging, by using contrast-enhanced intravascular ultrasound and differential image analysis. Int. Journal of Cardiology, 2008 (In Press).

12 12 I.A. Kakadiaris, U. Kurkure, A.N. Bandekar, S. O Malley and M. Naghavi (a) (b) (c) (d) FIGURE 1. Pericardial fat detection in CT. (a) Original CT image, (b) all labels overlaid after atlas initialization, (c) all labels after segmentation, and (d) pericardial fat overlaid. (a) (b) (c) (d) (e) FIGURE 2. Top: (a) Flowchart of an analysis of a contrast-enhanced IVUS sequence. From left to right - the original sequence, the sequence decimated by gating, the contour-tracking step, and difference imaging and overlay of results. Bottom: Demonstration of registration between histology and IVUS. (b) A stained histological image, (c) an IVUS image to which the histology image will be co-registered by manually defining corresponding landmarks, (d) deformed histology image based on the landmarks, and (e) the IVUS image highlighting the correspondence. Note the excellent agreement of our analysis with the histology.