Abstract:

Es wird eine Methode beschrieben, wie medizinische Bilder des Herzens modellbasiert mit EKG-Daten verknüpft werden können, um damit zu einer spezifischen Diagnostik und zu einer besseren Therapieplanung in der Kardiologie zu gelangen. Zunächst wird aus MRT- oder CT-Bildern des Patienten die Geometrie seines Herzens ermittelt. Elektrokardiographische Messungen an der Körperoberfläche (EKG oder Body Surface Potential Mapping) und aus dem Inneren des Herzens (intracardial mapping) werden aufgenommen und die Orte der Messung in den Bilddatensatz eingetragen (registration). Ein elektrophysiologisches Computermodell vom Herzen des Patienten wird mit Hilfe der elektrophysiologischen Messdaten iterativ angepasst. Schließlich entsteht im Computer ein virtuelles Herz des Patienten, welches sowohl die Geometrie als auch die Elektrophysiologie wiedergibt. Ein Modell der Vorhöfe hat beispielsweise das Potenzial, die Ursachen von Vorhofflimmern zu erkennen und die Radiofrequenz-Ablationsstrategie zu optimieren. Ein Modell der Ventrikel des Herzens kann helfen, genetisch bedingte Rhythmusstörungen besser zu verstehen oder auch die Parameter bei der kardialen Resynchronisationstherapie zu optimieren. Die Modellierung des Herzens mit einem Infarktgebiet könnte die elektrophysiologischen Auswirkungen des Infarktes beschreiben und die Risikostratifizierung für gefährliche ventrikuläre Arrhythmien unterstützen oder die Erfolgsrate bei ventrikulären Ablationen erhöhen.

Abstract:

After mathematical modeling of the healthy heart now modeling of diseases comes into the focus of research. Modeling of arrhythmias already shows a large degree of realism. This offers the chance of more detailed diagnosis and computer assisted therapy planning. Options for genetic diseases (channelopathies like Long-QT-syndrome), infarction and infarction-induced ventricular fibrillation, atrial fibrillation (AF) and cardiac resynchronization therapy are demonstrated.

Abstract:

Ventricular premature beats (VPB) occur when a cardiac depolarization is initiated from a focus in the ventricle instead of the sinoatrial node. Because the ventricular electrical excitation is not started from the intraventriclular conduction system, the excitation propagation in the ventricles behaves in an abnormal manner. This results in an extra asynchronous contraction of the ventricles. In addition VPBs can trigger life-threatening heart arrhythmias. Applying catheter ablation can cure VPB. Therefore it is of importance to localize the origin of VPB using a non-invasive approach before interventional treatment. In this work the inverse problem of electrocardiography is deployed to reconstruct electrical sources in the ventricles, from which the origin of VPB can be identified. By using a spatiotemporal maximum a posteriori (MAP) based regularization the quality of reconstructions is improved. In this work forward calculations with various VPBs are employed to construct a statistical a priori information.

Abstract:

Solving the forward problem of electrocardiography provides a better understanding of electrical activities in the heart. The inverse problem of electrocardiography enables a direct view of cardiac sources without catheter interventions. Today the forward and inverse computation is most often performed in a static model, which doesn't take into account the heart motion and may result in considerable errors in both forward and inverse solutions. In this work a dynamic heart model is developed. With this model the effect of the heart motion on the forward and inverse solutions is investigated.

Abstract:

Many studies conducted on patients suffering from congestive heart failure have shown the efficacy of cardiac resynchronization therapy (CRT). The presented research investigates an off-line optimization algorithm based on different electrode positioning and timing delays. A computer model of the heart was used to simulate left bundle branch block (LBBB), myocardial infarction (MI) and reduction of intraventricular conduction velocity in order to customize the patient symptom. The optimization method evaluates the error between the healthy heart and pathology with/without pacing in terms of activation time and QRS length. Additionally, a torso model of the patient is extracted to compute the body surface potential map (BSPM) and to simulate the ECG with Wilson leads to validate the results obtained by the electrophysiological heart model optimization.

Abstract:

The treatment of ventricular arrhythmia often requires detailed information about the location of ectopic beats. A noninvasive procedure is adopted to achieve this purpose. The aim of this work is the reconstruction of ectopic foci by using the critical point theory introduced by Greensite and Huiskamp [1]. The reliability and adaptability of the obtained simulation results are evaluated with regard to the reconstruction error.The reconstruction of bioelectrical sources from measured body surface potentials is an ill-posed problem and requires regularization. The advantage of the presented method is to deal with a well-posed formulation of the problem. Locations of ectopic beats can be detected by the critical point theorem. Four simulated ectopic centers have been localized to evaluate the method. The influence of Gaussian noise is considered.The reconstruction depends on the effective rank of a singular value decomposition (SVD) of the multi-channel ECG matrix. Regarding lower ranks, many critical points presenting ectopic foci can be observed. For higher ranks, the detection leads more and more to a stable estimation of ectopic locations. The detected critical points are shown to be reliable approximations of the simulated ectopic foci.