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.

No abstract

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 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.

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.

No abstract

Automatic Left Ventricle (LV) border detection in X-ray angiograms for the quantitative assessment of cardiac function has proven to be a highly challenging task. The main difficulty is segmenting the End Systolic (ES) phase, in which much of the contrast dye has been squeezed out of the LV due to contraction, resulting in poor LV definition. 2D Active Appearance Models (AAMs) have shown utility for segmenting End Diastolic (ED) angiograms, but do not perform satisfactory in individual ES angiograms. In this work, we present a new Multi-view AAM in which we exploit the existing correlation in shape and texture between ED and ES phase to steer the segmentation of both frames simultaneously. Model position and orientation remain independent, whereas appearance statistics are coupled. In addition, an AAM is presented in which the gray-value information of the inner part of the LV is not taken into account. This so-called boundary AAM is applied mainly to enhance local boundary localization performance. Both models are applied in a combined manner and are validated quantitatively. In 61 out of 70 experiments good convergence for both ED and ES segmentation was achieved, with average border positioning errors of 1.86 mm (ED) and 1.93 mm (ES).

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.