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Domain knowledge about the geometrical properties of cardiac structures is an important ingredient for the segmentation of those structures in medical images or for the simulation of cardiac physiology. So far, a strong focus was put on the left ventricle due to its importance for the general pumping performance of the heart and related functional indices. However, other cardiac structures are of similar importance, e.g. the coronary arteries with respect to diagnosis and treatment of arteriosclerosis or the left atrium with respect to the treatment of atrial fibrillation. In this paper we describe the generation of a comprehensive geometric cardiac model including the four cardiac chambers and the trunks of the connected vasculature, as well as the coronary arteries and a set of cardiac landmarks. A mean geometric model has been built. A general process to add inter-individual and temporal variability is proposed and will be added in a second stage.

For the analysis of shape variations of the heart and the cardiac motion in a clinical environment it is necessary to segment a large amount of data in order to be able to build statistically significant models. Therefore it has been the aim of this project to find and develop methods that allow the creation of a fully automatic segmentation pipeline for the segmentation of endocardium and myocardium in ECG-triggered MRI images. For this purpose a combination of a number of image processing techniques, from the fields of segmentation, modeling and image registration have been used and extended to create a segmentation pipeline that reduces the need for supplementary manual correction of the segmented labels to a minimum.

Segmentation of the fetal heart can facilitate the 3D assessment of the cardiac function and structure. Ultrasound acquisition typically results in drop-out artifacts of the chamber walls. This paper presents a level set deformable model to simultaneously segment all four cardiac chambers using region based information. The segmented boundaries are automatically penalized from intersecting at walls with signal dropout. Root mean square errors of the perpendicular distances between the algorithm’s delineation and manual tracings are within 7 pixels (<2mm) in 2D and under 3 voxels (<4.5mm) in 3D. The ejection fraction was determined from the 3D dataset. Future work will include further testing on additional datasets and validation on a phantom.

Calcified coronary arteries can cause severe cardiac problems and may provoke an infarction of the heart’s wall. An established treatment method is the bypass operation. The usage of a telemanipulation system allows for the execution of that operation as a totally endoscopic coronary artery bypass (TECAB) grafting. This relatively new method narrows the surgeon’s view and does not permit the palpation of the vessel in order to detect calcifications (hard plaques).

A planning based on contrast enhanced, cardiac CT data sets can compensate for that problem. This work presents analysis methods for coronary arteries. Hard plaques are detected using a tracking-based vessel segmentation technique. In addition, the vessel’s neighborhood is analyzed in order to decide whether it is surrounded by tissue or fat, or if it is freely accessible for the surgeon’s instruments. Furthermore, well adapted methods for the visualization of these analysis results are presented.

Magnetocardiographic (MCG) mapping measures magnetic fields generated by the electrophysiological activity of the heart. Quantitative analysis of MCG ventricular repolarization (VR) parameters may be useful to detect myocardial ischemia in patients with apparently normal ECG. However, manual calculation of MCG VR is time consuming and can be dependent on the examiner’s experience. Alternatively, the use of machine learning (ML) has been proposed recently to automate the interpretation of MCG recordings and to minimize human interference with the analysis. The aim of this study was to validate the predictive value of ML techniques in comparison with interactive, computer-aided, MCG analysis.

ML testing was done on a set of 140 randomly analysed MCG recordings from 74 subjects: 41 patients with ischemic heart disease (IHD) (group 1), 32 of them untreated (group 2), and 33 subjects without any evidence of cardiac disease (group 3). For each case at least 2 MCG datasets, recorded in different sessions, were analysed.

Two ML techniques combined identified abnormal VR in 25 IHD patients (group 1) and excluded VR abnormalities in 28 controls (group 3) providing 75% sensitivity, 85% specificity, 83% positive predictive value, 78% negative predictive value, 80% predictive accuracy This result was for the most part in agreement, but statistically better than that obtained with interactive analysis.

This study confirms that ML, applied on MCG recording at rest, has a predictive accuracy of 80% in detecting electrophysiological alterations associated with untreated IHD. Further work is needed to test the ML capability to differentiate VR alterations due to IHD from those due to non-ischemic cardiomyopathies.

Left ventricular hypertrophy induces remodeling of various ion channels and prolongs depolarization of the ventricles. We modified a model of electrical activity of rat ventricular cell by incorporating available experimental data. Hypertrophy was modeled by incorporating experimental data of changes in sodium (I), hyperpolarizing (I), outward transient potassium (I) and T-type calcium currents channel kinetics (I), cell size and Ca handling. In 1D simulations, a continuous increase in action potential duration (APD) and corresponding decrease in conduction velocity (CV) with subsequent beats was observed, resulting in conduction block at low values of stimulus intervals (SI), for which the simulated action potential (AP) restitution of the cell models has negative slope.

Effects of pathophysiological conditions and pharmacological intervention on transmural propagation are computed for the virtual ventricular wall. ST depression during sub-endocardial ischaemia and unidirectional functional block in the vulnerable window during Class III drug action are explained by changes induced in the transmural dispersion of action potential duration.

Electrophysiological heterogeneity within human ventricles is mainly based on differences of ion channel characteristics inside the wall. This influences also properties of cellular tension development.

In this work, knowledge about transmural heterogeneity was transferred to an electro-mechanical heart model composed of a human model describing electrophysiology and of a model for the development of tensions. The heterogeneity was included in the cardiomyocyte model by varying ion channel kinetics and density on basis of measured data. The properties of the heterogeneous electro-mechanical model were demonstrated in a realistic model of left ventricular geometry and fiber orientation using a monodomain approach for describing electrical interaction.

This study indicated the necessity of incorporating regional heterogeneity to model human cardiac electro-mechanics with qualitative good agreement to measured data. The heterogeneity leads to a homogenization of the mechanical process due to increasing time to peak tension from epicardium towards endocardium.

Recent findings in a sheep model of atrial fibrillation support the hypothesis that an organized micro-reentry could be the maintaining mechanism of the arrhythmia (mother wavelet). According to these studies we constructed a two dimensional computer model of tissue in the region around a pair of pulmonary vein ostia and investigated anchoring of a reentry wave at these ostia. We used the Luo Rudy phase I ionic current model to describe membrane kinetics and generated two different stages of electrical remodelling of the cells by varying the slow inward calcium current. Our attempt to initiate a stable reentry failed for cells with higher action potential duration and higher rate adaption. By simulating a higher stadium of electrical remodelling we finally were successful, and we were able to produce a periodic reentry. This led us to the conclusion that a low rate adaption (high electrical remodelling) facilitates organized activity in the atria.

We report on an investigation into using a Level Sets based method to reconstruct activation wavefronts at each time instant from measured potentials on the body surface. The potential map on the epicardium is approximated by a two level image and the inverse problem is solved by evolving a boundary, starting from an initial region, such that a filtered residual error is minimized. The advantage of this method over standard activation-based solutions is that no isotropy assumptions are required. We discuss modifications of the Level Sets method used to improve accuracy, and show the promise of this method via simulation results using recorded canine epicardial data.