Transmural distribution and connectivity of coronary collaterals within the human heart.

Despite the importance of collateral vessels in human hearts, a detailed analysis of their distribution within the coronary vasculature based on three-dimensional vascular reconstructions is lacking. This study aimed to classify the transmural distribution and connectivity of coronary collaterals in human hearts. One normotrophic human heart and one hypertrophied human heart with fibrosis in the inferior wall from a previous infarction were obtained. After filling the coronary arteries with fluorescent replica material, hearts were frozen and alternately cut and block-face imaged using an imaging cryomicrotome. Transmural distribution, connectivity, and diameter of collaterals were determined. Numerous collateral vessels were found (normotrophic heart: 12.3 collaterals/cm(3); hypertrophied heart: 3.7 collaterals/cm(3)), with 97% and 92%, respectively, of the collaterals located within the perfusion territories (intracoronary collaterals). In the normotrophic heart, intracoronary collaterals {median diameter [interquartile range (IQR)]: 91.4 [73.0-115.7] µm} were most prevalent (74%) within the left anterior descending (LAD) territory. Intercoronary collaterals [median diameter (IQR): 94.3 (79.9-107.4) µm] were almost exclusively (99%) found between the LAD and the left circumflex artery (LCX). In the hypertrophied heart, intracoronary collaterals [median diameter (IQR): 101.1 (84.8-126.0) µm] were located within both the LAD (48%) and LCX (46%) territory. Intercoronary collaterals [median diameter (IQR): 97.8 (89.3-111.2) µm] were most prevalent between the LAD-LCX (68%) and LAD-right coronary artery (28%). This study shows that human hearts have abundant coronary collaterals within all flow territories and layers of the heart. The majority of these collaterals are small intracoronary collaterals, which would have remained undetected by clinical imaging techniques.

Automatic detection and quantification of the Agatston coronary artery calcium score on contrast computed tomography angiography.

Potentially, Agatston coronary artery calcium (CAC) score could be calculated on contrast computed tomography coronary angiography (CTA). This will make a separate non-contrast CT scan superfluous. This study aims to assess the performance of a novel fully automatic algorithm to detect and quantify the Agatston CAC score in contrast CTA images. From a clinical registry, 20 patients were randomly selected for each CAC category (i.e. 0, 1-99, 100-399, 400-999, ≥1,000). The Agatston CAC score on non-contrast CT was calculated manually, while the novel algorithm was used to automatically detect and quantify Agatston CAC score in contrast CTA images. The resulting Agatston CAC scores were validated against the non-contrast images. A total of 100 patients (60 ± 11 years, 63 men) were included. The median CAC score on non-contrast CT was 145 (IQR 5-760), whereas the contrast CTA CAC score was 170 (IQR 23-594) (P = 0.004). The automatically computed CAC score showed a high correlation (R = 0.949; P < 0.001) and intra-class correlation (R = 0.863; P < 0.001) with non-contrast CT CAC score. Moreover, agreement within CAC categories was good (κ 0.588). Fully automatic detection of Agatston CAC score on contrast CTA is feasible and showed high correlation with non-contrast CT CAC score. This could imply a radiation dose reduction and time saving by omitting the non-contrast scan.

A conditional statistical shape model with integrated error estimation of the conditions; application to liver segmentation in non-contrast CT images.

This paper presents a novel conditional statistical shape model in which the condition can be relaxed instead of being treated as a hard constraint. The major contribution of this paper is the integration of an error model that estimates the reliability of the observed conditional features and subsequently relaxes the conditional statistical shape model accordingly. A three-step pipeline consisting of (1) conditional feature extraction from a maximum a posteriori estimation, (2) shape prior estimation through the novel level set based conditional statistical shape model with integrated error model and (3) subsequent graph cuts segmentation based on the estimated shape prior is applied to automatic liver segmentation from non-contrast abdominal CT volumes. Comparison with three other state of the art methods shows the superior performance of the proposed algorithm.

Automated left ventricular delineation in X-ray angiograms: a validation study.

OBJECTIVES: Recently an automated analysis approach for left ventricular (LV) X-ray angiographic studies was proposed. This particular study aims to assess the clinical potential of this approach. BACKGROUND: Over the past 30 years much research has been carried out to develop a technique with automated contour detection of the LV outline in the end-diastolic (ED) and end-systolic (ES) phases. Very few have made it into clinical practice. Our latest approach is based on innovative model-based image processing techniques. METHODS: Two expert cardiologists analyzed 30 patient studies both by contouring the LV manually and by using the proposed automated methodology. In the latter procedure the experts were allowed to edit the automatically generated contours manually. The manual, automatic, and edited automatic contours were compared, focusing on accuracy, workflow efficiency, and inter- and intra-observer variabilities. RESULTS: No significant differences between the automatically derived and manual LV volumes were observed. The average patient study analysis time was reduced by 26%, from 4.2 to 3.1 min. When editing was required, 19% of the ED and 25% of the ES contour length needed manual correction. Furthermore, a reduction in inter-observer variability of 12.4% was observed. CONCLUSIONS: Using the proposed automated methodology for X-ray LV angiographic study analysis, a considerable reduction in required analysis time and manual effort is achieved. Because the acquired results are of clinically acceptable quality and the inter- and intra-observer variabilities are reduced, this automated approach has the potential to optimize the analysis workflow for LV X-ray angiography in clinical practice.

Automated contour detection in cardiac MRI using active appearance models: the effect of the composition of the training set.

OBJECTIVE: Definition of the optimal training set for the automated segmentation of short-axis left ventricular magnetic resonance (MR) imaging studies in clinical practice based on active appearance model (AAM). MATERIALS AND METHODS: We investigated the segmentation accuracy by varying the size and composition of the training set (ie, the ratio between pathologic and normal ventricle images, and the vendor dependence). The accuracy was assessed using the degree of similarity and the difference in ejection fraction between automatically detected and manually drawn contours. RESULTS: Including more images in the training set results in a better accuracy of the detected contours, with optimum results achieved when including 180 images in the training set. Using AAM-based contour detection with a mixed model of 80% normal-20% pathologic images does provide good segmentation accuracy in clinical routine. Finally, it is essential to define different AAM models for different vendors of MRI systems. CONCLUSIONS: A model defined on a sufficient number of images with the correct distribution of image characteristics achieves good matches in clinical routine. It is essential to define different AAM models for different vendors of MRI systems.

Automated contour detection in X-ray left ventricular angiograms using multiview active appearance models and dynamic programming.

This paper describes a new approach to the automated segmentation of X-ray left ventricular (LV) angiograms, based on active appearance models (AAMs) and dynamic programming. A coupling of shape and texture information between the end-diastolic (ED) and end-systolic (ES) frame was achieved by constructing a multiview AAM. Over-constraining of the model was compensated for by employing dynamic programming, integrating both intensity and motion features in the cost function. Two applications are compared: a semi-automatic method with manual model initialization, and a fully automatic algorithm. The first proved to be highly robust and accurate, demonstrating high clinical relevance. Based on experiments involving 70 patient data sets, the algorithm's success rate was 100% for ED and 99% for ES, with average unsigned border positioning errors of 0.68 mm for ED and 1.45 mm for ES. Calculated volumes were accurate and unbiased. The fully automatic algorithm, with intrinsically less user interaction was less robust, but showed a high potential, mostly due to a controlled gradient descent in updating the model parameters. The success rate of the fully automatic method was 91% for ED and 83% for ES, with average unsigned border positioning errors of 0.79 mm for ED and 1.55 mm for ES.