Efficient Patient-Specific Simulations of Ventricular Tachycardia Based on Computed Tomography-Defined Wall Thickness Heterogeneity.

BACKGROUND: Electrophysiological mapping of ventricular tachycardia (VT) is tedious and poorly reproducible. Substrate analysis on imaging cannot explicitly display VT circuits. OBJECTIVES: This study sought to introduce a computed tomography-based model personalization approach, allowing for the simulation of postinfarction VT in a clinically compatible time frame. METHODS: In 10 patients (age 65 ± 11 years, 9 male) referred for post-VT ablation, computed tomography-derived wall thickness maps were registered to 25 electroanatomical maps (sinus rhythm, paced, and VT). The relationship between wall thickness and electrophysiological characteristics (activation-recovery interval) was analyzed. Wall thickness was then employed to parameterize a fast and tractable organ-scale wave propagation model. Pacing protocols were simulated from multiple sites to test VT induction in silico. In silico VTs were compared to VT circuits mapped clinically. RESULTS: Clinically, 6 different VTs could be induced with detailed maps in 9 patients. The proposed model allowed for fast simulation (median: 6 min/pacing site). Simulations of steady pacing (600 milliseconds) from 100 different sites/patient never triggered any arrhythmia. Applying S1-S2 or S1-S2-S3 induction schemes allowed for the induction of in silico VTs in the 9 of 10 patients who were clinically inducible. The patient who was not inducible clinically was also noninducible in silico. A total of 42 different VTs were simulated (4.2 ± 2 per patient). Six in silico VTs matched a VT circuit mapped clinically. CONCLUSIONS: The proposed framework allows for personalized simulations in a matter of hours. In 6 of 9 patients, simulations show re-entrant patterns matching intracardiac recordings.

Deep learning formulation of electrocardiographic imaging integrating image and signal information with data-driven regularization.

AIMS: Electrocardiographic imaging (ECGI) is a promising tool to map the electrical activity of the heart non-invasively using body surface potentials (BSP). However, it is still challenging due to the mathematically ill-posed nature of the inverse problem to solve. Novel approaches leveraging progress in artificial intelligence could alleviate these difficulties. METHODS AND RESULTS: We propose a deep learning (DL) formulation of ECGI in order to learn the statistical relation between BSP and cardiac activation. The presented method is based on Conditional Variational AutoEncoders using deep generative neural networks. To quantify the accuracy of this method, we simulated activation maps and BSP data on six cardiac anatomies.We evaluated our model by training it on five different cardiac anatomies (5000 activation maps) and by testing it on a new patient anatomy over 200 activation maps. Due to the probabilistic property of our method, we predicted 10 distinct activation maps for each BSP data. The proposed method is able to generate volumetric activation maps with a good accuracy on the simulated data: the mean absolute error is 9.40 ms with 2.16 ms standard deviation on this testing set. CONCLUSION: The proposed formulation of ECGI enables to naturally include imaging information in the estimation of cardiac electrical activity from BSP. It naturally takes into account all the spatio-temporal correlations present in the data. We believe these features can help improve ECGI results.

3D MRI of explanted sheep hearts with submillimeter isotropic spatial resolution: comparison between diffusion tensor and structure tensor imaging.

OBJECTIVE: The aim of the study is to compare structure tensor imaging (STI) with diffusion tensor imaging (DTI) of the sheep heart (approximately the same size as the human heart). MATERIALS AND METHODS: MRI acquisition on three sheep ex vivo hearts was performed at 9.4 T/30 cm with a seven-element RF coil. 3D FLASH with an isotropic resolution of 150 µm and 3D spin-echo DTI at 600 µm were performed. Tensor analysis, angles extraction and segments divisions were performed on both volumes. RESULTS: A 3D FLASH allows for visualization of the detailed structure of the left and right ventricles. The helix angle determined using DTI and STI exhibited a smooth transmural change from the endocardium to the epicardium. Both the helix and transverse angles were similar between techniques. Sheetlet organization exhibited the same pattern in both acquisitions, but local angle differences were seen and identified in 17 segments representation. DISCUSSION: This study demonstrated the feasibility of high-resolution MRI for studying the myocyte and myolaminar architecture of sheep hearts. We presented the results of STI on three whole sheep ex vivo hearts and demonstrated a good correspondence between DTI and STI.

Are wall thickness channels defined by computed tomography predictive of isthmuses of postinfarction ventricular tachycardia?

BACKGROUND: Wall thickness (WT) in post-myocardial infarction scar is heterogenous, with channels of relatively preserved thickness bordered by thinner scar. OBJECTIVE: This study sought to determine whether 3-dimensionally-reconstructed computed tomography (CT) channels correlate with electrophysiological isthmuses during ventricular tachycardia (VT). METHODS: We retrospectively studied 9 postinfarction patients (aged 57 ± 15 years, 1 female) with 10 complete VT activation maps (cycle length 429 ± 77ms) created using high-resolution mapping. Three-dimensionally-reconstructed WT maps from CT were merged with the activation map during sinus rhythm (SR) and VT. The relationship between WT and electrophysiological characteristics was analyzed. RESULTS: A total of 41 CT channels were identified (median 4 per patient), of median (range) length 21.2 mm (17.3-36.8 mm), width 9.0 mm (6.7-16.5 mm), and area 1.49 cm2(1.00-1.75 cm2). WT in the channel was significantly thicker in the center than in the edge (median 2.4 mm vs 1.5 mm, P < .0001). Of 3163 (2493-5960) mapping points in SR, 382 (191-1115) local abnormal ventricular activities (LAVAs) were identified. One patient had a maximal proportion of LAVAs in 3-4 mm, 3 patients in 2-3 mm, 2 in 1-2 mm, and 2 in 0-1 mm. The VT isthmuses of all 10 VTs corresponded with 1-4 CT channels. Twenty-one of the 41 CT channels (51.2%) corresponded to a VT isthmus (entrance, mid, or exit). Electrophysiological VT isthmuses were more likely to be associated with CT channels that were longer (P = .04, odds ratio [OR] 1.05/mm), thinner (but not less than 1 mm) (P = .03, OR 0.36/mm), or parallel to the mitral annulus (P = .07, OR 3.93). CONCLUSION: VT isthmuses were always found in CT channels (100% sensitivity), and half of CT channels hosted VT isthmuses (positive predictive value 51%). Longer and thinner (but >1 mm) CT channels were significantly associated with VT isthmuses.

Fast personalized electrophysiological models from computed tomography images for ventricular tachycardia ablation planning.

AIMS: Clinical application of patient-specific cardiac computer models requires fast and robust processing pipelines that can be seamlessly integrated into clinical workflows. We aim at building such a pipeline from computed tomography (CT) images to personalized cardiac electrophysiology (EP) model. The simulation output could be useful in the context of post-infarct ventricular tachycardia (VT) radiofrequency ablation (RFA) planning for pre-operative targets prediction. METHODS AND RESULTS: The support for model personalization is a patient-specific virtual three-dimensional heart obtained from CT images. Here, the scar is identified as thinning of the myocardial wall on automatically computed thickness maps. We then use an Eikonal model of wave front propagation with reduced velocity in the damaged areas. An image-based vessel enhancement algorithm can automatically identify VT isthmuses. The personalized model is used for virtual pacing. We obtained a very fast pipeline that enables simulations in only a few minutes. It is fully automated starting from the semi-automated image segmentation phase. The computational time frame is compatible with the construction of a virtual pacing tool. In this tool, onset points and an optional directional block could be interactively selected. The directional block is a simple way to model tissue refractoriness. Output activation maps are compared with EP data acquired pre-operatively. We show that this framework allows the reproduction of recorded re-entrant VT activation patterns. CONCLUSION: Our simulation framework has an application in VT RFA intervention planning. It could be used to guide EP explorations and even predict ablation targets pre-operatively. This could reduce intervention duration and improve success rate.