Gerd Brunner

Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data.

Measurements related to coronary artery calcification (CAC) offer significant predictive value for coronary artery disease (CAD). In current medical practice CAC scoring is a labor-intensive task. The objective of this paper is the development and evaluation of a family of coronary artery region (CAR) models applied to the detection of CACs in coronary artery zones and sections. Thirty patients underwent non-contrast electron-beam computed tomography scanning. Coronary artery trajectory points as presented in the University of Houston heart-centered coordinate system were utilized to construct the CAR models which automatically detect coronary artery zones and sections. On a per-patient and per-zone basis the proposed CAR models detected CACs with a sensitivity, specificity and accuracy of 85.56 (±15.80)%, 93.54 (±1.98)%, and 85.27 (±14.67)%, respectively while the corresponding values in the zones and segments based case were 77.94 (±7.78)%, 96.57 (±4.90)%, and 73.58 (±8.96)%, respectively. The results of this study suggest that the family of CAR models provide an effective method to detect different regions of the coronaries. Further, the CAR classifiers are able to detect CACs with a mean sensitivity and specificity of 86.33 and 93.78%, respectively.

High-Fat Feeding-Induced Hyperinsulinemia Increases Cardiac Glucose Uptake and Mitochondrial Function Despite Peripheral Insulin Resistance

In obesity, reduced cardiac glucose uptake and mitochondrial abnormalities are putative causes of cardiac dysfunction. However, high-fat diet (HFD) does not consistently induce cardiac insulin resistance and mitochondrial damage, and recent studies suggest HFD may be cardioprotective. To determine cardiac responses to HFD, we investigated cardiac function, glucose uptake, and mitochondrial respiration in young (3-month-old) and middle-aged (MA) (12-month-old) male Ldlr(-/-) mice fed chow or 3 months HFD to induce obesity, systemic insulin resistance, and hyperinsulinemia. In MA Ldlr(-/-) mice, HFD induced accelerated atherosclerosis and nonalcoholic steatohepatitis, common complications of human obesity. Surprisingly, HFD-fed mice demonstrated increased cardiac glucose uptake, which was most prominent in MA mice, in the absence of cardiac contractile dysfunction or hypertrophy. Moreover, hearts of HFD-fed mice had enhanced mitochondrial oxidation of palmitoyl carnitine, glutamate, and succinate and greater basal insulin signaling compared with those of chow-fed mice, suggesting cardiac insulin sensitivity was maintained, despite systemic insulin resistance. Streptozotocin-induced ablation of insulin production markedly reduced cardiac glucose uptake and mitochondrial dysfunction in HFD-fed, but not in chow-fed, mice. Insulin injection reversed these effects, suggesting that insulin may protect cardiac mitochondria during HFD. These results have implications for cardiac metabolism and preservation of mitochondrial function in obesity.

A supervised classification-based method for coronary calcium detection in non-contrast CT.

Accurate quantification of coronary artery calcium provides an opportunity to assess the extent of atherosclerosis disease. Coronary calcification burden has been reported to be associated with cardiovascular risk. Currently, an observer has to identify the coronary calcifications among a set of candidate regions, obtained by thresholding and connected component labeling, by clicking on them. To relieve the observer of such a labor-intensive task, an automated tool is needed that can detect and quantify the coronary calcifications. However, the diverse and heterogeneous nature of the candidate regions poses a significant challenge. In this paper, we investigate a supervised classification-based approach to distinguish the coronary calcifications from all the candidate regions and propose a two-stage, hierarchical classifier for automated coronary calcium detection. At each stage, we learn an ensemble of classifiers where each classifier is a cost-sensitive learner trained on a distinct asymmetrically sampled data subset. We compute the relative location of the calcifications with respect to a heart-centered coordinate system, and also use the neighboring regions of the calcifications to better characterize their properties for discrimination. Our method detected coronary calcifications with an accuracy, sensitivity and specificity of 98.27, 92.07 and 98.62%, respectively, for a testing dataset of non-contrast computed tomography scans from 105 subjects.

Automatic quantification of muscle volumes in magnetic resonance imaging scans of the lower extremities.

Muscle volume measurements are essential for an array of diseases ranging from peripheral arterial disease, muscular dystrophies, neurological conditions to sport injuries and aging. In the clinical setting, muscle volume is not routinely measured due to the lack of standardized ways for its repeatable quantification. In this paper, we present magnetic resonance muscle quantification (MRMQ), a method for the automatic quantification of thigh muscle volume in magnetic resonance imaging (MRI) scans. MRMQ integrates a thigh segmentation and nonuniform image gradient correction step, followed by feature extraction and classification. The classification step leverages prior probabilities, introducing prior knowledge to a maximum a posteriori classifier. MRMQ was validated on 344 slices taken from 60 MRI scans. Experiments for the fully automatic detection of muscle volume in MRI scans demonstrated an averaged accuracy, sensitivity and specificity for leave-one-out cross-validation of 88.3%, 93.6% and 87.2%, respectively.

Fuzzy-Cuts: A knowledge-driven graph-based method for medical image segmentation

Image segmentation is, in general, an ill-posed problem and additional constraints need to be imposed in order to achieve the desired result. Particularly in the field of medical image segmentation, a significant amount of prior knowledge is available that can be used to constrain the solution space of the segmentation problem. However, most of this prior knowledge is, in general, vague or imprecise in nature, which makes it very difficult to model. This is the problem that is addressed in this paper. Specifically, in this paper, we present fuzzy-cuts, a novel, knowledge-driven, graph-based method for medical image segmentation. We cast the problem of image segmentation as the maximum a posteriori (MAP) estimation of a Markov random field (MRF) which, in essence, is equivalent to the minimization of the corresponding Gibbs energy function. Considering the inherent imprecision that is common in the a priori description of objects in medical images, we propose a fuzzy theoretic model to incorporate knowledge-driven constraints into the MAP-MRF formulation. In particular, we focus on prior information about the objects location, appearance and spatial connectivity to a known seed region inside the object. To that end, we introduce fuzzy connectivity and fuzzy location priors that are used in combination to define the first-order clique potential of the Gibbs energy function. In our experiments, we demonstrate the application of the proposed method to the challenging problem of heart segmentation in non-contrast computed tomography (CT) data.

The Effect of Lipid Modification on Peripheral Artery Disease after Endovascular Intervention Trial (ELIMIT)

METHODS A total of 102 patients were randomized to either mono-therapy with simvastatin (40 mg daily) or triple-therapy with simvastatin (40 mg daily), extended-release niacin (1500 mg daily), and ezetimibe (10 mg daily). MRI was performed at baseline and 6, 12, and 24 months. SFA wall, lumen, and total vessel volumes were quantified. MRI-derived SFA parameters and lipids were analyzed with multilevel models and nonparametric tests, respectively. RESULTS Baseline characteristics did not differ between mono and triple-therapy groups, except for ethnicity (p = 0.02). SFA wall, lumen, and total vessel volumes increased non-significantly for both groups between baseline and 24-months. Non-high-density lipoprotein cholesterol was significantly reduced at 12 months with triple-therapy compared with mono-therapy (p = 0.01). CONCLUSION No significant differences were observed between mono-therapy using simvastatin and triple-therapy with simvastatin, extended-release niacin, and ezetimibe for 24-month changes in SFA wall, lumen, and total vessel volumes. CLINICAL TRIAL REGISTRATION INFORMATION NCT00687076; Link: http://clinicaltrials.gov/ct2/show/NCT00687076.

Toward Unsupervised Classification of Calcified Arterial Lesions

There is growing evidence that calcified arterial deposits play a crucial role in the pathogenesis of cardiovascular disease. This paper investigates the challenging problem of unsupervised calcified lesion classification. We propose an algorithm, US-CALC (UnSupervised Calcified Arterial Lesion Classification), that discriminates arterial lesions from non-arterial lesions. The proposed method first mines the characteristics of calcified lesions using a novel optimization criterion and then identifies a subset of lesion features which is optimal for classification. Second, a two stage clustering is deployed to discriminate between arterial and non-arterial lesions. A histogram intersection distance measure is incorporated to determine cluster proximity. The clustering hierarchies are carefully validated and the final clusters are determined by a new intracluster compactness measure. Experimental results indicate an average accuracy of approximately 80% on a database of electron beam CT heart scans.

Magnetic resonance imaging-based computational modelling of blood flow and nanomedicine deposition in patients with peripheral arterial disease.

Peripheral arterial disease (PAD) is generally attributed to the progressive vascular accumulation of lipoproteins and circulating monocytes in the vessel walls leading to the formation of atherosclerotic plaques. This is known to be regulated by the local vascular geometry, haemodynamics and biophysical conditions. Here, an isogeometric analysis framework is proposed to analyse the blood flow and vascular deposition of circulating nanoparticles (NPs) into the superficial femoral artery (SFA) of a PAD patient. The local geometry of the blood vessel and the haemodynamic conditions are derived from magnetic resonance imaging (MRI), performed at baseline and at 24 months post intervention. A dramatic improvement in blood flow dynamics is observed post intervention. A 500% increase in peak flow rate is measured in vivo as a consequence of luminal enlargement. Furthermore, blood flow simulations reveal a 32% drop in the mean oscillatory shear index, indicating reduced disturbed flow post intervention. The same patient information (vascular geometry and blood flow) is used to predict in silico in a simulation of the vascular deposition of systemically injected nanomedicines. NPs, targeted to inflammatory vascular molecules including VCAM-1, E-selectin and ICAM-1, are predicted to preferentially accumulate near the stenosis in the baseline configuration, with VCAM-1 providing the highest accumulation (approx. 1.33 and 1.50 times higher concentration than that of ICAM-1 and E-selectin, respectively). Such selective deposition of NPs within the stenosis could be effectively used for the detection and treatment of plaques forming in the SFA. The presented MRI-based computational protocol can be used to analyse data from clinical trials to explore possible correlations between haemodynamics and disease progression in PAD patients, and potentially predict disease occurrence as well as the outcome of an intervention.

Morphometric analysis of calcification and fibrous layer thickness in carotid endarterectomy tissues

BACKGROUND Advanced atherosclerotic lesions are commonly characterized by the presence of calcification. Several studies indicate that extensive calcification is associated with plaque stability, yet recent studies suggest that calcification morphology and location may adversely affect the mechanical stability of atherosclerotic plaques. The underlying cause of atherosclerotic calcification and the importance of intra-plaque calcium distribution remains poorly understood. METHOD The goal of this study was the characterization of calcification morphology based on histological features in 20 human carotid endarterectomy (CEA) specimens. Representative frozen sections (10μm thick) were cut from the common, bulb, internal and external segments of CEA tissues and stained with von Kossa׳s reagent for calcium phosphate. The morphology of calcification (calcified patches) and fibrous layer thickness were quantified in 135 histological sections. RESULTS Intra-plaque calcification was distributed heterogeneously (calcification %-area: bulb segment: 14.2±2.1%; internal segment: 12.9±2.8%; common segment: 4.6±1.1%; p=0.001). Calcified patches were found in 20 CEAs (patch size: <0.1mm(2) to >1.0mm(2)). Calcified patches were most abundant in the bulb and least in the common segment (bulb n=7.30±1.08; internal n=4.81±1.17; common n=2.56±0.56; p=0.0007). Calcified patch circularity decreased with increasing size (<0.1mm(2): 0.77±0.01, 0.1-1mm(2): 0.62±0.01, >1.0mm(2): 0.51±0.02; p=0.0001). A reduced fibrous layer thickness was associated with increased calcium patch size (p<0.0001). CONCLUSIONS In advanced carotid atherosclerosis, calcification appears to be a heterogeneous and dynamic atherosclerotic plaque component, as indicated by the simultaneous presence of few large stabilizing calcified patches and numerous small calcific patches. Future studies are needed to elucidate the associations of intra-plaque calcification size and distribution with atherothrombotic events.

Simultaneous bilateral magnetic resonance imaging of the femoral arteries in peripheral arterial disease patients.

To image the femoral arteries in peripheral arterial disease (PAD) patients using a bilateral receive coil.