Computational atrial models aid the understanding of pathological mechanisms and therapeutic measures in basic research. The use of biophysical models in a clinical environment requires methods to personalize the anatomy and electrophysiology (EP). Strategies for the automation of model generation and for evaluation are needed. In this manuscript, the current efforts of clinical atrial modeling in the euHeart project are summarized within the context of recent publications in this field. Model-based segmentation methods allow for the automatic generation of ready-to-simulate patient-specific anatomical models. EP models can be adapted to patient groups based on a-priori knowledge, and to the individual without significant further data acquisition. ECG and intracardiac data build the basis for excitation personalization. Information from late enhancement (LE) MRI can be used to evaluate the success of radio-frequency ablation (RFA) procedures and interactive virtual atria pave the way for RFA planning. Atrial modeling is currently in a transition from the sole use in basic research to future clinical applications. The proposed methods build the framework for model-based diagnosis and therapy evaluation and planning. Complex models allow to understand biophysical mechanisms and enable the development of simplified models for clinical applications.
ECG imaging is a non-invasive technique of characterizing the electrical activity and the corresponding excitation conduction of the heart using body surface ECG. The method may provide great opportunities in the planning of cardiac interventions and in the diagnosis of cardiac diseases. This work introduces an algorithm for the imaging of transmembrane voltages that is based on a Kalman filter with an augmented measurement model. In the latter, a regularization term is integrated as additional measurement. The filter is trained using a-priori-knowledge from a simulation model. Two effects are investigated: the influence of the training data on the reconstruction quality and the representation of a-priori knowledge in the trained covariance matrices. The proposed algorithm shows a promising quality of reconstruction and may be used in the future to introduce generic physiological knowledge in solutions of cardiac source imaging.
With ECG imaging it is possible to reconstruct cardiac electrical activity noninvasively from measurements of the electrocardiogram (ECG). To facilitate the recon- struction, an MRI- or CT- based model of the body is re- quired, which is represented as a volume conductor. A mathematically ill-posed problem is solved to reconstruct the cardiac sources from potentials collected on the body surface. To obtain a body surface potential map (BSPM) electrodes are ideally placed allover the entire thorax. In practical applications, however, the number of electrodes is limited and the placing is subject to constraints. We in- vestigate the effect of different electrode setups on the ill- posedness of the inverse problem. In particular, electrode setups are chosen to comply with constraints for female pa- tients in the catheter lab.
A new method to predict changes in a lead-field matrix induced by conductivity variations of a single body tissue is proposed. The approach is based on the princi- ple component analysis (PCA) with three initial lead-field matrices transformed to vectors as input. For each tissue blood, lungs, muscles and fat a PCA was carried out. Further, for each tissue the default conductivity value and the conductivity varied by ±50 % were used to calculate the sample lead-field matrices. The results of the PCAs in- dicate that for every tissue the first principle component suffices to predict the conductivity-induced changes in the samples. With an interpolation of the scores we addition- ally show that the prediction is not bound to the sample ma- trices but moreover every matrix within each conductivity range is possibly estimated and conclusively predicted.
The inverse problem of ECG is the task of cardiac source reconstruction from the measured body surface potential maps (BSPM). It is ill posed and therefore requires regularization, which is usually applied uniformly to the whole heart geometry. In order to improve the solution quality and localize potentials extrema we propose a local regularization method: the weighting is done iteratively according to the solution spatial content. The performed test showed the ability of the new method to overcome over smoothing and to better reconstruct strong solution gradients.
D. Potyagaylo, M. Segel, W. H. W. Schulze, and O. Dössel. Noninvasive Localization of Ectopic Foci: a New Optimization Approach for Simultaneous Reconstruction of Transmembrane Voltages and Epicardial Potentials. In FIMH, LNCS 7945, pp. 166-173, 2013
The goal of ECG imaging is the reconstruction of cardiac electrical activities from the potentials measured on the thorax sur- face. The tool can gain prominent clinical value for diagnosis and pre- interventional planning. The problem is however ill-posed, i.e. it is highly sensitive to modelling and measurement errors. In order to overcome this obstacle a regularization technique must be applied. In this paper we pro- pose a new optimization based method for simultaneous reconstruction of transmembrane voltages and epicardial potentials for localizing the origin of ventricular ectopic beats.Compared to second-order Tikhonov regularization, the new approach showed superior performance in marking activated regions and provided meaningful results where Tikhonov method failed.
Body surface potential mapping (BSPM) can be used to non- invasively measure the electrical activity of the heart using a dense set of thorax electrodes and a CT/MR scan of the thorax to solve the inverse problem of electrophysiology (ECGi). This technique now shows potential clinical value for the assessment and treatment of patients with arrhythmias. Co-localisation of the electrode positions and the CT/MR thorax scan is essential. This manuscript describes a method to perform the co-localisation using multiple biplane X-ray images. The electrodes are automatically detected and paired in the X-ray images. Then the 3D positions of the electrodes are computed and mapped onto the thorax surface derived from CT/MR. The proposed method is based on a multi-scale blob detection algorithm and the generalized Hough transform, which can automatically discriminate the leads used for BSPM from other ECG leads. The pairing method is based on epi-polar constraint matching and line pattern detection which assumes that BSPM electrodes are arranged in strips. The proposed methods are tested on a thorax phantom and two clinical cases. Results show an accuracy of 0.33 ± 0.20mm for detecting electrodes in the X-ray images and a success rate of 95.4%. The automatic pairing method achieves a 91.2% success rate.