Computer modeling of bioelectric activity is of interest in cardiac electrophysiology [1Gulrajani RM, Geselowitz DB. Models of the electrical activity of the heart and computer simulation of the electrocardiogram Crit Rev Biomed Eng 1988; 16: 1-66.-3Modre R, Tilg B, Fischer G, Wach P. An iterative algorithm for myocardial activation time imaging Comput Methods Programs Biomed 2001; 64: 1-7.] and, furthermore, the non-invasive imaging of the electrical excitation from electrocardiogram (ECG) has become a diagnosis tool in clinical applications [4Ramanathan C, Ghanem R, Jia P, Ryu K, Rudy Y. Noninvasive electrocardiographic imaging for cardiac electrophysiology and arrhythmia Nat Med 2004; 10(4): 422-28., 5Modre R, Tilg B, Fischer G, et al. Atrial noninvasive activation mapping of paced rhythm data J Cardiovasc Electrophysiol 2003; 14(7): 712-19.]. For non-invasive imaging of the electrical function four dimensional (3D and time) anatomical and ECG mapping data have to be combined. This enables the computation of the electrocardiographic inverse problem, which is commonly referred to as the estimation of the source from measured fields. The anatomical data are obtained via magnetic resonance imaging (MRI). These volume data provide the basis for generating a patient-specific volume conductor model (VCM). In order to solve the inverse problem for imaging the electrical excitation a VCM is needed. A VCM comprises the electrically most active compartments: chest surface, lung surface, atrial and ventricular myocardium and the cardiac blood masses [6Bradley C, Pullan A, Hunter P. Effects of material properties and geometry on electrocardiographic forward simulations Ann Biomed Eng 2000; 28(7): 721-41.]. The ECG mapping data are acquired with a multi-channel ECG recording system [7Metting van Rijn AC, Peper A, Grimbergen CA. High quality recording of bioelectric events. Part 1. Interference reduction, theory and practice Med Biol Eng Comput 1990; 28(5): 389-97., 8Metting van Rijn AC, Peper A, Grimbergen CA. High quality recording of bioelectric events.Part 2. Low-noise, low-power multichannel amplifier design Med Biol Eng Comput 1991; 29(4): 433-0.]. In the electrocardiographic inverse problem, the electrical source distribution is reconstructed from body surface maps by employing an electrical source-model description. Because of the generally unknown individual cardiac fiber orientation, electrical isotropy is assumed in the surface heart model approach based on the bidomain theory [9Greensite F. The mathematical basis for imaging cardiac electrical function Crit Rev Biomed Eng 1994; 22: 347-99.-11Tilg B, Fischer G, Modre R, et al. Model-based imaging of cardiac electrical excitation in humans IEEE Trans Med Imaging 2002; 21(9): 1031-39.].

The cardiac electrical source imaging technique provides information about the spread of electrical excitation in order to assist the cardiologist in developing strategies for treatment of cardiac arrhythmias. In many cases it is possible to trace back common cardiac arrhythmias to accessory pathways, atrial or ventricular foci, e.g., from the pulmonary veins [12Haissaguerre M, Jais P, Shah DC, et al. Spontaneous initiation of atrial fibrillation by ectopic beats originating from the pulmonary veins N Engl J Med 1998; 339(10): 659-6.], and reentrant circuits [13Vigmond EJ, Ruckdeschel R, Trayanova NA. Reentry in a morphologically realistic atrial model J Cardiovasc Electrophysiol 2001; 12(9): 1046-54.]. Identifying the site of origin of the ectopic focus or the location of an accessory pathway provides essential information for treatment strategies, like catheter ablation [14Morady F. Radio-frequency ablation as treatment for cardiac arrhythmias N Engl J Med 1999; 340(7): 534-44.].

In clinical practice the localization of ectopic origins is currently achieved by traditional catheter techniques and by recently introduced catheter mapping systems, like the CARTO™ system [15Andrea E, Atie J, Maciel W, et al. Mapping of supraventricular tachycardias by using a new tridimensional technology — The CARTO system J Electrocardiol 2001; 34(4): 334.]. The major advantage of the CARTO™ system is that it enables the combination between electrical and spatial endocardial information, which supports the cardiologist in getting a deeper insight in the patients’ arrhythmias. CARTO™ provides electro-anatomical information that assists the cardiologist in applying linear lesions. CARTO™ shows significant limitations when it comes to acquiring single-beat activation maps and, furthermore, these are mostly applied to single target chambers only. Another important fact is that it is impossible to obtain information from the epicardium under clinical conditions. Moreover, this invasive technique imposes a higher risk to the patient during the procedure. These limitations can be bypassed by providing the electrical excitation non-invasively, by applying the non-invasive imaging of cardiac electrophysiology (NICE) approach. The goal was the development of a modeling pipeline that enables our approach to become clinically applicable.

To enable non-invasive cardiac imaging a modus operandi was defined that needs to be followed in order to process the pipeline. In the first modeling step the anatomy of the patient has to be acquired. This is performed by MRI using a Magnetom-Vision-Plus 1.5 T (Siemens Medical Solutions) scanner. The atrial and ventricular geometry is recorded in CINE-mode during breath-hold (expiration, short-axis scans, 4-6 mm spacing). The shape of the lungs and the torso are recorded in T1-FLASH-mode during breath-hold (expiration, long-axis scans, 10 mm spacing). For locating and coupling the mapping electrodes vitamin E capsules were used, because these markers are clearly visible in the MRI images and can be easily identified. Seven markers (anatomical landmarks on the anterior and lateral chest wall) were used to couple the locations of the electrodes to the MRI frame. Eleven capsules were attached on the back, in order to tag the positions of the posterior electrodes, which were not accessible during the electrophysiology study (EPS). The positions of the anterior and lateral electrodes were digitized using the Fastrak system (Polhemus Inc.). Additionally, the locations of the 7 anatomical landmarks were digitized in order to allow transformation into the MRI frame. A VCM with visualized electrode positions using spheres is depicted in Fig. (1).

Fig. (1) Volume conductor model generated from 3D cine MRI data with two axial MR scans with overlaid electrode positions (segmented liquid capsules) from a left lateral oblique view; also shown: the triangular model of the chest surface, the surface models of the both lungs (LL, RL), and the ventricular model (RV, LV).

The next step is the volume conductor modeling task using two volume data sets. The short-axis data is used for the cardiac modeling of the blood masses, the ventricles’ myocardium and the atria. The axial data set is used for modeling the lung and chest surfaces. When the extraction procedure is finished the compartments are coupled together to the so-called volume conductor model. This modeling part needs to be done in a short time period because the time between the MRI procedure and the catheter intervention is normally limited by two hours.

The third step is accomplished in the catheter laboratory and is known as EPS. The electrical recordings from different locations in the heart and the response to electrical stimulation via the wires allow detailed information about the cause of the palpitations to be identified. The EPS is a diagnostic procedure to obtain a reading of the impulses, which cause irregular heart beats from the interior of the heart. If the diseased location is found the cardiologist medicates via ablation [14Morady F. Radio-frequency ablation as treatment for cardiac arrhythmias N Engl J Med 1999; 340(7): 534-44.]. When estimating the electrical excitation a multi-lead ECG is derived from the patients’ body surface. The derived ECG signals are then pre-filtered and baseline corrected. With this initial estimation the cardiologist is able to find the location of the accessory pathway by using the standard mapping system CARTO™ (Biosense Webster Inc.) and disconnects the pathway by ablation [14Morady F. Radio-frequency ablation as treatment for cardiac arrhythmias N Engl J Med 1999; 340(7): 534-44.].

After this procedure the accessory pathways location is estimated (in case of e.g., Wolff-Parkinson-White (WPW) Syndrome) using the NICE [3Modre R, Tilg B, Fischer G, Wach P. An iterative algorithm for myocardial activation time imaging Comput Methods Programs Biomed 2001; 64: 1-7., 10Modre R, Tilg B, Fischer G, Wach P. A noninvasive Myocardial Activation Time Imaging: A novel Inverse Algorithm applied to Clinical ECG Mapping Data IEEE Trans Biomed Eng 2002; 49(10): 1153-61., 11Tilg B, Fischer G, Modre R, et al. Model-based imaging of cardiac electrical excitation in humans IEEE Trans Med Imaging 2002; 21(9): 1031-39.] approach.

When the EPS study is finished all measured data of the NICE pipeline including the CARTO™maps are analyzed in order to validate the quality of the models and the non-invasive estimation of the electrical excitation in the human heart.

When facing the challenge to solve the ill-posed problem of non-invasive cardiac electrical source imaging many circumstances will influence the result. First of all, the forward problem has a unique solution whereas the inverse problem cannot be solved uniquely. Because of this, the inverse problem depends on any available a-priori information. One also needs to be careful in the representation of the data uncertainties. Furthermore, the ECG signal has to be of high quality, the model error of the volume conductor needs to be as small as possible, and the coupling of the different coordinate systems (MRI coordinate system, global coordinate system of the electrodes, CARTO™) also needs to be done carefully to keep the global model error low, because small changes in the system may affect the result dramatically.

The CARTO™maps created during the EPS by the cardiologist represent the gold standard with regard to electroanatomical function in the target chamber. Therefore, after the interventional treatment all relevant data are compared with the gold standard to assess the quality of the NICE approach [5Modre R, Tilg B, Fischer G, et al. Atrial noninvasive activation mapping of paced rhythm data J Cardiovasc Electrophysiol 2003; 14(7): 712-19., 34Tilg B, Hanser F, Modre R. Clinical ECG mapping and imaging of cardiac electrical excitation J Electrocardiol 2002; 35: 81-7., 35Tilg B, Fischer G, Modre R, Hanser F, Messnarz B, Roithinger FX. Electrocardiographic imaging of atrial and ventricular electrical activation Med Image Anal 2003; 7(3): 391-98.].

In addition to this check, the generated models, using the AAM approach, were compared to an expert segmentation. Due to the fact that there is no gold standard in segmentation, the human experts’ segmentation was assumed to be the gold standard. Therefore, the blood mass models were extracted by a human expert and the AAM approach, and the segmentation results (binary images) were evaluated in reference to their similarity. The binary Dice [36Dice LR. Measures of the amount of ecologic association between species Ecology 1945; 26(3): 297-302.] similarity coefficient was used to compute the similarity between the approaches. The Dice coefficient was calculated for the ventricular and atrial blood mass models, which were created by a human expert, and the ones created by the AAM approach. The mean coefficient for the ventricular and atrial blood mass is 0.8917 and 0.8665, respectively. The Dice binary similarity coefficient is defined as:

where A and B are the image objects, the operator |.| computes the cardinality of a set and ∩ finds the intersection of two sets.

The patient-specific volume conductor modeling approach was tested in eight different patients [11Tilg B, Fischer G, Modre R, et al. Model-based imaging of cardiac electrical excitation in humans IEEE Trans Med Imaging 2002; 21(9): 1031-39.]. Fig. (5) shows a triangulated representation of the patient-specific chest and the separated lungs, using an axial scan. The left image (last row) in Fig. (2) shows the chest and the lungs superimposed to the MRI volume data.

Fig. (5) Extracted and triangulated surface meshes of the chest (left) and the separated lungs (right) in an anterior-posterior view.

The cardiac models were extracted based on a cine-gated short-axis scan. Fig. (6) shows the triangulated left and right atria and the ventricular model with its blood masses. The right image (last row) in Fig. (2) shows the atria and the ventricles superimposed to the short-axis cine-gated MRI volume data.

Fig. (6) Cardiac models of the coupled left and right atria (left figure) and the ventricular model with the related blood masses (right figure) of two patients.

These compartments are combined together to the so-called patient-specific volume conductor model that enables the estimation of the electrical excitation in the heart. Fig. (7) illustrates the early activation time map (the early activation time is colored in red; the accessory pathway is located left lateral), computed with our software package.

Fig. (7) Result of an activation time estimation. The cardiac model is shown in a cranial view. The accessory pathway that caused the WPW syndrome is colored red at a left lateral position. The two spheres mark the position where the cardiologist successfully ablated the accessory pathway using the CARTO™ system.

The positions at which the cardiologist performed the ablation procedure to disconnect the accessory pathway that caused the Wolff Parkinson White (WPW) syndrome is marked by spheres. The difference of the estimated position and the position found by the cardiologist using the CARTO™system is about 1 cm. The reason for this localization difference is mainly due to the above mentioned measurement errors and the fact that the inverse problem solution can only be computed by approximation. One has to keep in mind, however, that the CARTO™system does not provide exact solutions as well.

The overall calculation time for estimating the activation time maps was in the same order of the time necessary for mapping the basal area of the ventricles using CARTO™. The mean segmentation time for a patient-specific model was about 25 min using a Pentium IV with 3.2 GHz clock speed and 1 GB of main memory (RAM). Compared to a manual segmentation procedure the segmentation pipeline enables the non-invasive cardiac imaging process to become clinically applicable. Furthermore, with the activation time imaging approach maps can be computed for both chambers, whereas the conventional CARTO™ system is able to image only one target chamber.

The goal of this work was to develop a method for constructing a patient-specific volume conductor model in a fast and accurate way in order to estimate the electrical excitation in a human functional heart model for clinical applications. The development of the MST framework allows creating tissue-specific segmentation pipelines due to the clear, homogenous and, thus, easy-to-use interface. The possibility to integrate different frameworks into MST enables to increase functionality without increasing complexity.

The extraction of the ventricular and atrial blood masses yielded the same quality as the ones obtained by human experts. The myocardium extraction or estimation, in case the a-priori information had to be used, proved successful, but with the addition that the complex geometry of the atria with its orifices (especially the pulmonary veins) needed sometimes little manual corrections.

By obtaining the morphological image data using computed tomography (CT) the accuracy of the myocardium could be increased, but the radiation exposure and the use of a contrast agent does not legitimate this procedure. Furthermore, the ill-posed nature of the approach along with the smoothing properties of the Fredholm integral limits the quality of the results anyway.

At this time the VCM is a static one; the ventricular and the atrial model characterize the systole of the heart. Although it is possible to generate a patient-specific cardiac model at different trigger stages using this approach, the calculated time-dependent leadfield matrix L, needed for performing the simulation, would result in an extreme amount of data and computational time - in an exponential order. Theoretically, such a time-dependent leadfield matrix would allow a more precise simulation, the calculation time and the handling of these large data sets would render the problem insolvable for a state-of-the-art workstation.

When performing non-invasive cardiac imaging many different steps have to be combined. For achieving best possible results the whole non-invasive cardiac imaging workflow needs to be processed precisely to keep the mean model error at a low level.

The creation of a semi-automatic volume conductor modeling pipeline enables the NICE approach to be clinically applicable. The effort to create a modeling pipeline that is able to extract the relevant compartments in a short time with high accuracy increased the overall model quality and decreased the user interaction complexity.

Our clinical NICE studies show that this technique is feasible and supports the cardiologist with important information.