Stefan C. Saur

Choosing the optimal wall shear parameter for the prediction of plaque location—A patient-specific computational study in human right coronary arteries

OBJECTIVE While the correlation of atherosclerotic plaque locations with local wall shear stress magnitude has been evaluated previously by other investigators in both right (RCA) and left coronary arteries (LCA), the relative performance of average wall shear stress (AWSS), average wall shear stress gradient (AWSSG), oscillatory shear index (OSI) and relative residence time (RRT) as indicators of potential atherosclerotic plaque locations has not been studied for the LCA. Here we determine the performance of said wall shear parameters in the LCA for the prediction of plaque development locations and compare these results to those previously found in the RCA. METHODS We obtained 30 patient-specific geometries (mean age 67.1 (± 9.2) years, all with stable angina) of the LCA using dual-source computed tomography and virtually removed any plaque present. We then performed computational fluid dynamics simulations to calculate the wall shear parameters. RESULTS For the 96 total plaques, AWSS had a higher sensitivity for the prediction of plaque locations (86 ± 25%) than AWSSG (65 ± 37%, p<0.05), OSI (67 ± 32%, p<0.01) or RRT (48 ± 38%, p<0.001). RRT had a higher PPV (49 ± 36%) than AWSS (31 ± 20%, p<0.05) or AWSSG (16 ± 12%, p<0.001). Segment 5 of the LCA presented with overall low values for sensitivity and PPV. Parameter performance in the remainder of the LCA was comparable to that in the RCA. CONCLUSIONS AWSS features remarkably high sensitivity, but does not reach the PPV of RRT. This may indicate that while low wall shear stress is necessary for plaque formation, its presence alone is not sufficient to predict future plaque locations. Time dependent factors have to be taken into account as well.

Patient-specific three-dimensional simulation of LDL accumulation in a human left coronary artery in its healthy and atherosclerotic states

We calculate low-density lipoprotein (LDL) transport from blood into arterial walls in a three-dimensional, patient-specific model of a human left coronary artery. The in vivo anatomy data are obtained from computed tomography images of a patient with coronary artery disease. Models of the artery anatomy in its healthy and diseased states are derived after segmentation of the vessel lumen, with and without the detected plaque, respectively. Spatial shear stress distribution at the endothelium is determined through the reconstruction of the arterial blood flow field using computational fluid dynamics. The arterial endothelium is represented by a shear stress-dependent, three-pore model, taking into account blood plasma and LDL passage through normal junctions, leaky junctions, and the vesicular pathway. Intraluminal pressures of 70 and 120 mmHg are employed as the normal and hypertensive operating pressures, respectively. By applying our model to both the healthy and diseased states, we show that the location of the plaque in the diseased state corresponds to one of the two sites with predicted high-LDL concentration in the healthy state. We further show that, in the diseased state, the site with high-LDL concentration has shifted distal to the plaque, which is in agreement with the clinical observation that plaques generally grow in the downstream direction. We also demonstrate that hypertension leads to increased number of regions with high-LDL concentration, elucidating one of the ways in which hypertension may promote atherosclerosis.

Automatic Detection of Calcified Coronary Plaques in Computed Tomography Data Sets

The detection of calcified plaques is an essential step in the assessment of coronary heart diseases. However, manual plaque segmentation is subjected to intra- and inter-observer variability. We present a novel framework for the automatic detection of calcified coronary plaques in Computed Tomography images. In contrast to the state-of-the-art, both the native and the angio data sets are included to gain additional information about each plaque for its detection and subsequent assessment. The framework was successfully tested on 127 patients where 85.5% of the calcified and 96% of the obstructive plaques have been detected.

Automatic Ascending Aorta Detection in CTA Datasets

The assessment of coronary arteries is an essential step when diagnosing coronary heart diseases. There exists a wide range of specialized algorithms for the segmentation of the coronary arteries in Computed Tomography Angiography datasets. In general, these algorithms have to be initialized by manually placing a seed point at the origins of the coronary arteries or within the ascending aorta. In this paper we present a fast and robust algorithm for the automatic detection of the ascending aorta in Computed Tomography Angiography datasets using a two-level threshold ray propagation approach. We further combine this method with an aorta segmentation and coronary artery tree detection algorithm to achieve a fully automatic coronary artery segmentation.

Identification of Atherosclerotic Lesion-Prone Sites through Patient-Specific Simulation of Low-Density Lipoprotein Accumulation

We present a patient-specific model of low-density lipoprotein (LDL) transport from blood into arterial walls. To this end, the arterial endothelium is represented by a shear-stress dependent three-pore model taking into account blood plasma and LDL passage through the vesicular pathway, normal junctions and leaky junctions. We virtually remove atherosclerotic plaque from an in-vivo left coronary artery computed tomography (CT) dataset to obtain an approximation of the artery anatomy in its healthy state. By applying our model, we show that the location of the plaque in the diseased state corresponds to one of the two sites with predicted high LDL concentration in the healthy state. We further show that in the diseased state, the site with high LDL concentration has shifted distally, which is in agreement with the clinical observation that plaques generally grow in downstream direction.

ACCURATUM: improved calcium volume scoring using a mesh-based algorithm—a phantom study

To overcome the limitations of the classical volume scoring method for quantifying coronary calcifications, including accuracy, variability between examinations, and dependency on plaque density and acquisition parameters, a mesh-based volume measurement method has been developed. It was evaluated and compared with the classical volume scoring method for accuracy, i.e., the normalized volume (measured volume/ground-truthed volume), and for variability between examinations (standard deviation of accuracy). A cardiac computed-tomography (CT) phantom containing various cylindrical calcifications was scanned using different tube voltages and reconstruction kernels, at various positions and orientations on the CT table and using different slice thicknesses. Mean accuracy for all plaques was significantly higher (p < 0.0001) for the proposed method (1.220 ± 0.507) than for the classical volume score (1.896 ± 1.095). In contrast to the classical volume score, plaque density (p = 0.84), reconstruction kernel (p = 0.19), and tube voltage (p = 0.27) had no impact on the accuracy of the developed method. In conclusion, the method presented herein is more accurate than classical calcium scoring and is less dependent on tube voltage, reconstruction kernel, and plaque density.

Prediction rules for the detection of coronary artery plaques: evidence from cardiac CT.

Objectives:To evaluate spatial plaque distribution patterns in coronary arteries based on computed tomography coronary angiography data sets and to express the learned patterns in prediction rules. An application is proposed to use these prediction rules for the detection of initially missed plaques. Material and Methods:Two hundred fifty two consecutive patients with chronic coronary artery disease underwent contrast-enhanced dual-source computed tomography coronary angiography for clinical indications. Coronary artery plaques were manually labeled on a 16-segment coronary model and their position (ie, segments and bifurcations) and composition (ie, calcified, mixed, or noncalcified) were noted. The frequent itemset mining algorithm was used to statistically search for plaque distribution patterns. The patterns were expressed as prediction rules: given plaques at certain locations as conditions, a prediction rule gave evidence—with a certain confidence value—for a plaque at another location within the coronary artery tree. Prediction rules with the highest confidence values were evaluated and described. Furthermore, to improve manual plaque detection, all prediction rules were applied on the patient data to search for segments with potentially missed plaques. These segments were then reviewed in a second, guided reading for the existence of plaques. The same number of segments was also determined by a weighted random approach to evaluate the quality of prediction resulting from frequent itemset mining. Results:In 200 of 252 (79.4%) patients, at least one coronary plaque (range, 1–22 plaques) was found. In total 1229 plaques (990 calcified, 80.6%; 227 mixed, 18.5%; 12 noncalcified, 1%) distributed, over 916 coronary segments and 507 vessels were manually labeled. Four plaque distribution patterns were identified: 20.6% of the patients had no plaques at all; 31.7% had plaques in the left coronary artery tree; 46.4% had plaques both in left and right coronary arteries, whereas 1.2% of the patients had plaques solely in the right coronary artery (RCA). General rules were found predicting plaques in the left anterior descending artery (LAD), given plaques in segments of the RCA or in the left main artery. Further general rules predicted plaques in the LAD, given plaques in the circumflex artery. In the guided review, the segment selection based on the prediction rules from frequent itemset mining performed significantly better (P < 0.001) than the weighted random approach by revealing 48 initially missed plaques. Conclusions:This study demonstrates spatial plaque distribution patterns in coronary arteries as determined with cardiac CT. Use of the frequent itemset mining algorithm yielded rules that predicted plaques at certain sites given plaques at other sites of the coronary artery tree. Use of these prediction rules improved the manual labeling of coronary plaques as initially missed plaques could be predicted with the guided review.

Contrast enhancement with dual energy CT for the assessment of atherosclerosis

A drawback of the commonly used single source computed tomography systems (CT) is that different materials might show very similar attenuation at any selected radiation energy. However, the assessment of atherosclerosis requires good differentiation between vessel lumen, calcium, adipose, and surrounding tissue. Dual energy CT (DECT) simultaneously measures attenuations at two energies and therefore can improve the differentiation to some extent. A tissue cancelation and enhancement algorithm for dual energy data was already proposed in 1981 and evaluated on experimental settings with a stationary X-ray source. For this study, we adapted this algorithm for DECT and propose its usage as a pre-processing step for the assessment of atherosclerosis. On clinical DECT patient data and with fixed parameters we could show a simultaneous contrast enhancement between 8% and 67% among all targeted tissues.