Comparing pulsed field electroporation and radiofrequency ablation for the treatment of paroxysmal atrial fibrillation: design and rationale of the BEAT PAROX-AF randomized clinical trial.

AIMS: Using thermal-based energy sources [radiofrequency (RF) energy/cryo energy] for catheter ablation is considered effective and safe when performing pulmonary vein isolation (PVI) in patients with paroxysmal atrial fibrillation (AF). However, treatment success remains limited and complications can occur due to the propagation of thermal energy into non-target tissues. We aim to compare pulsed field ablation (PFA) with RF ablation in terms of efficacy and safety for patients with drug-resistant paroxysmal AF. METHODS AND RESULTS: The BEAT PAROX-AF trial is a European multicentre, superiority, open-label randomized clinical trial in two parallel groups. A total of 292 participants were recruited in 9 high-volume European clinical centres in 5 countries. Patients with paroxysmal AF were randomized to PFA (FARAPULSE Endocardial Ablation System©, Boston Scientific) or RF using the CLOSE protocol with contact force sensing catheter (SmartTouch© catheter and CARTO© Biosense Webster). The primary endpoint will be the 1-year recurrence of atrial arrhythmia, and the major secondary safety endpoint will be the occurrence of acute (<7 days) procedure-related serious adverse events, or pulmonary vein stenosis, or atrio-oesophageal fistula up to 12 months. Additionally, five sub-studies investigate the effect of PFA on oesophageal safety, cerebral lesions, cardiac autonomic nervous system, durability of PVI as assessed during redo ablation procedures, and atrial and ventricular function. The study began on 27 December 2021 and concluded recruitment on 17 January 2024. Results will be available in mid-2025. CONCLUSION: The BEAT PAROX-AF trial aims to provide critical insights into the optimal treatment approach for patients with paroxysmal AF.

Automated inversion time selection for black-blood late gadolinium enhancement cardiac imaging in clinical practice.

OBJECTIVE: To simplify black-blood late gadolinium enhancement (BL-LGE) cardiac imaging in clinical practice using an image-based algorithm for automated inversion time (TI) selection. MATERIALS AND METHODS: The algorithm selects from BL-LGE TI scout images, the TI corresponding to the image with the highest number of sub-threshold pixels within a region of interest (ROI) encompassing the blood-pool and myocardium. The threshold value corresponds to the most recurrent pixel intensity of all scout images within the ROI. ROI dimensions were optimized in 40 patients' scans. The algorithm was validated retrospectively (80 patients) versus two experts and tested prospectively (5 patients) on a 1.5 T clinical scanner. RESULTS: Automated TI selection took ~ 40 ms per dataset (manual: ~ 17 s). Fleiss' kappa coefficient for automated-manual, intra-observer and inter-observer agreements were [Formula: see text]= 0.73, [Formula: see text] = 0.70 and [Formula: see text] = 0.63, respectively. The agreement between the algorithm and any expert was better than the agreement between the two experts or between two selections of one expert. DISCUSSION: Thanks to its good performance and simplicity of implementation, the proposed algorithm is a good candidate for automated BL-LGE imaging in clinical practice.

Advanced Imaging Integration for Catheter Ablation of Ventricular Tachycardia.

PURPOSE OF REVIEW: Imaging plays a crucial role in the therapy of ventricular tachycardia (VT). We offer an overview of the different methods and provide information on their use in a clinical setting. RECENT FINDINGS: The use of imaging in VT has progressed recently. Intracardiac echography facilitates catheter navigation and the targeting of moving intracardiac structures. Integration of pre-procedural CT or MRI allows for targeting the VT substrate, with major expected impact on VT ablation efficacy and efficiency. Advances in computational modeling may further enhance the performance of imaging, giving access to pre-operative simulation of VT. These advances in non-invasive diagnosis are increasingly being coupled with non-invasive approaches for therapy delivery. This review highlights the latest research on the use of imaging in VT procedures. Image-based strategies are progressively shifting from using images as an adjunct tool to electrophysiological techniques, to an integration of imaging as a central element of the treatment strategy.

Preoperative personalization of atrial fibrillation ablation strategy to prevent esophageal injury: Impact of changes in esophageal position.

INTRODUCTION: Due to changes in esophageal position, preoperative assessment of the esophageal location may not mitigate the risk of esophageal injury in catheter ablation for atrial fibrillation (AF). This study aimed to assess esophageal motion and its impact on AF ablation strategies. METHODS AND RESULTS: Ninety-seven AF patients underwent two computed tomography (CT) scans. The area at risk of esophageal injury (AAR) was defined as the left atrial surface ≤3 mm from the esophagus. On CT1, ablation lines were drawn blinded to the esophageal location to create three ablation sets: individual pulmonary vein isolation (PVI), wide antral circumferential ablation (WACA), and WACA with linear ablation (WACA + L). Thereafter, ablation lines for WACA and WACA + L were personalized to avoid the AAR. Rigid registration was performed to align CT1 onto CT2, and the relationship between ablation lines and the AAR on CT2 was analyzed. The esophagus moved by 3.6 [2.7 to 5.5] mm. The AAR on CT2 was 8.6 ± 3.3 cm2 , with 77% overlapping that on CT1. High body mass index was associated with the AAR mismatch (standardized ß 0.382, p < .001). Without personalization, AARs on ablation lines for individual PVI, WACA, and WACA + L were 0 [0-0.4], 0.8 [0.5-1.2], and 1.7 [1.2-2.0] cm2 . Despite the esophageal position change, the personalization of ablation lines for WACA and WACA + L reduced the AAR on lines to 0 [0-0.5] and 0.7 [0.3-1.0] cm2 (p < .001 for both). CONCLUSION: The personalization of ablation lines based on a preoperative CT reduced ablation to the AAR despite changes in esophageal position.

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.

Value of 3D right ventricular function over 2D assessment in acute pulmonary embolism.

BACKGROUND: Pulmonary embolism (PE) is a common life-threatening disease, with mortality related to right ventricular (RV) dysfunction. AIMS: To investigate the value of 3D global and regional RV strain in patients with acute PE and at 1 month, as compared to a control population. METHODS AND RESULTS: We conducted a longitudinal case-control prospective study, including 24 consecutive intermediate-risk PE patients. All patients underwent 2D and 3D transthoracic echocardiography within 12 hours of PE diagnosis and 1 month after hospital discharge. A control group was recruited, consisting of healthy volunteers matched on age and sex with PE patients. 3D RV echocardiographic sequences were analyzed by commercial RV-specific software and output meshes were post-processed to extract regional deformation. 3D echocardiographic 1-month follow-up was available in 18 patients. During acute PE, area strain was substantially altered in the RV free wall and within the trabecular septum. PE patients initially had RV dysfunction as assessed by 2D and 3D parameters. At follow-up, 2D parameters were restored compared to the control group, contrary to 3D RV area and circumferential strains. The McConnell's sign was identified in 83% of patients and was associated with reduced apical and global RV area strain. CONCLUSIONS: Our 3D RV strain study demonstrates an incomplete recovery of 3D strain parameters 1 month after an episode of intermediate-risk acute PE despite restored 2D parameters. Further studies are required to assess the prognostic role and implications of this residual RV strain impairment after PE.

Cardiac Imaging in Patients With Ventricular Tachycardia.

Ventricular tachycardia (VT) is a major cause of sudden cardiac death. The majority of malignant VTs occur in patients with structural heart disease. Multimodality imaging techniques play an integral role in determining the underlying etiology and prognostic significance of VT. In recent years, advances in imaging technology have enabled characterization of the structural arrhythmogenic substrate in patients with VT with increasing precision. In parallel with these advances, the role of cardiac imaging has expanded from a largely diagnostic tool to an adjunctive tool to guide interventional approaches for treatment of VT. Invasive and noninvasive imaging techniques, often used in combination, have made it possible to integrate structural and electrophysiological information during VT ablation procedures. An important area of current development is the use of noninvasive imaging techniques based on body surface electrocardiographic mapping to elucidate the mechanisms of VT. In the future, these techniques may provide a priori information on mechanisms of VT in patients undergoing interventional procedures. This review provides an overview of the role of cardiac imaging in patients with VT.

Correlation between computer tomography-derived scar topography and critical ablation sites in postinfarction ventricular tachycardia.

BACKGROUND: Myocardial wall thickness (WT) in patients with a prior myocardial infarction has been used to indicate scarring. However, the correlation of WT with sites critical to ventricular tachycardia (VT) has not been previously investigated. The purpose of this study was to correlate electroanatomic mapping data obtained during VT ablation with WT determined by cardiac computed tomography (CT). METHODS AND RESULTS: Cardiac CTs were performed in 15 consecutive patients (mean age 63 ± 10 years, 86% male, left ventricular ejection fraction 27 ± 12%) with a prior infarct referred for VT ablation. The CTs were registered to the electroanatomic maps obtained during the mapping procedure. Pacing was performed throughout the scar at sites with fractionated electrograms and isolated potentials. Ablation sites were identified by pace-mapping or entrainment-mapping and these sites were correlated with WT. Bipolar and unipolar voltage amplitude and bipolar electrogram width correlated with WT (correlation coefficient: 0.63, 0.65, and 0.41, respectively, P < 0.001). Ablation target sites were identified for 58 of 113 inducible VTs. The ablation target sites were located on CT-defined ridges (WT: 4.2 ± 1.2 mm) bordered by areas of thinning (WT: 2.6 ± 1.1 mm, P < 0.0001) in 14 of 15 patients. Ablation targets were found on ridges in 49 of 58 VTs (84%) for which target sites were identified. A total of 70 ridges were localized in the 15 patients. VT became noninducible postablation in 11 of 15 patients (73%). CONCLUSION: WT measured by CT identifies ridges of myocardial tissue that often are critical for postinfarction VT and that can be appropriate target sites for ablation.

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.

Image Integration to Guide Catheter Ablation in Scar-Related Ventricular Tachycardia.

BACKGROUND: Although multi-detector computed tomography (MDCT) and cardiac magnetic resonance (CMR) can assess the structural substrate of ventricular tachycardia (VT) in ischemic cardiomyopathy (ICM), non-ICM (NICM), and arrhythmogenic right ventricular cardiomyopathy (ARVC), the usefulness of systematic image integration during VT ablation remains undetermined. METHODS AND RESULTS: A total of 116 consecutive patients (67 ICM; 30 NICM; 19 ARVC) underwent VT ablation with image integration (MDCT 91%; CMR 30%; both 22%). Substrate was defined as wall thinning on MDCT and late gadolinium-enhancement on CMR in ICM/NICM, and as myocardial hypo-attenuation on MDCT in ARVC. This substrate was compared to mapping and ablation results with the endpoint of complete elimination of local abnormal ventricular activity (LAVA), and the impact of image integration on procedural management was analyzed. Imaging-derived substrate identified 89% of critical VT isthmuses and 85% of LAVA, and was more efficient in identifying LAVA in ICM and ARVC than in NICM (90% and 90% vs. 72%, P < 0.0001), and when defined from CMR than MDCT (ICM: 92% vs. 88%, P = 0.026, NICM: 88% vs. 72%, P < 0.001). Image integration motivated additional mapping and epicardial access in 57% and 33% of patients. Coronary and phrenic nerve integration modified epicardial ablation strategy in 43% of patients. The impact of image integration on procedural management was higher in ARVC/NICM than in ICM (P < 0.01), and higher in case of epicardial approach (P < 0.0001). CONCLUSIONS: Image integration is feasible in large series of patients, provides information on VT substrate, and impacts procedural management, particularly in ARVC/NICM, and in case of epicardial approach.

Biophysical Modeling Predicts Ventricular Tachycardia Inducibility and Circuit Morphology: A Combined Clinical Validation and Computer Modeling Approach.

INTRODUCTION: Computational modeling of cardiac arrhythmogenesis and arrhythmia maintenance has made a significant contribution to the understanding of the underlying mechanisms of arrhythmia. We hypothesized that a cardiac model using personalized electro-anatomical parameters could define the underlying ventricular tachycardia (VT) substrate and predict reentrant VT circuits. We used a combined modeling and clinical approach in order to validate the concept. METHODS AND RESULTS: Non-contact electroanatomic mapping studies were performed in 7 patients (5 ischemics, 2 non-ischemics). Three ischemic cardiomyopathy patients underwent a clinical VT stimulation study. Anatomical information was obtained from cardiac magnetic resonance imaging (CMR) including high-resolution scar imaging. A simplified biophysical mono-domain action potential model personalized with the patients' anatomical and electrical information was used to perform in silico VT stimulation studies for comparison. The personalized in silico VT stimulations were able to predict VT inducibility as well as the macroscopic characteristics of the VT circuits in patients who had clinical VT stimulation studies. The patients with positive clinical VT stimulation studies had wider distribution of action potential duration restitution curve (APD-RC) slopes and APDs than the patient with a negative VT stimulation study. The exit points of reentrant VT circuits encompassed a higher percentage of the maximum APD-RC slope compared to the scar and non-scar areas, 32%, 4%, and 0.2%, respectively. CONCLUSIONS: VT stimulation studies can be simulated in silico using a personalized biophysical cardiac model. Myocardial spatial heterogeneity of APD restitution properties and conductivity may help predict the location of crucial entry/exit points of reentrant VT circuits.

A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta.

BACKGROUND: Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. METHODS: Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient's anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. RESULTS: The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. CONCLUSIONS: The suggested method has the potential to discover previously unknown 3D shape biomarkers from medical imaging data. Thus, it could contribute to improving diagnosis and risk stratification in complex cardiac disease.

Automated Quantification of Right Ventricular Fat at Contrast-enhanced Cardiac Multidetector CT in Arrhythmogenic Right Ventricular Cardiomyopathy.

PURPOSE: To evaluate an automated method for the quantification of fat in the right ventricular (RV) free wall on multidetector computed tomography (CT) images and assess its diagnostic value in arrhythmogenic RV cardiomyopathy (ARVC). MATERIALS AND METHODS: This study was approved by the institutional review board, and all patients gave informed consent. Thirty-six patients with ARVC (mean age ± standard deviation, 46 years ± 15; seven women) were compared with 36 age- and sex-matched subjects with no structural heart disease (control group), as well as 36 patients with ischemic cardiomyopathy (ischemic group). Patients underwent contrast material-enhanced electrocardiography-gated cardiac multidetector CT. A 2-mm-thick RV free wall layer was automatically segmented and myocardial fat, expressed as percentage of RV free wall, was quantified as pixels with attenuation less than -10 HU. Patient-specific segmentations were registered to a template to study fat distribution. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic value of fat quantification by using task force criteria as a reference. RESULTS: Fat extent was 16.5% ± 6.1 in ARVC and 4.6% ± 2.7 in non-ARVC (P < .0001). No significant difference was observed between control and ischemic groups (P = .23). A fat extent threshold of 8.5% of RV free wall was used to diagnose ARVC with 94% sensitivity (95% confidence interval [CI]: 82%, 98%) and 92% specificity (95% CI: 83%, 96%). This diagnostic performance was higher than the one for RV volume (mean area under the ROC curve, 0.96 ± 0.02 vs 0.88 ± 0.04; P = .009). In patients with ARVC, fat correlated to RV volume (R = 0.63, P < .0001), RV function (R = -0.67, P = .001), epsilon waves (R = 0.39, P = .02), inverted T waves in V1-V3 (R = 0.38, P = .02), and presence of PKP2 mutations (R = 0.59, P = .02). Fat distribution differed between patients with ARVC and those without, with posterolateral RV wall being the most ARVC-specific area. CONCLUSION: Automated quantification of RV myocardial fat on multidetector CT images is feasible and performs better than RV volume in the diagnosis of ARVC. Online supplemental material is available for this article.

Cardiac arrythmias: multimodal assessment integrating body surface ECG mapping into cardiac imaging.

PURPOSE: To demonstrate the feasibility of comprehensive assessment of cardiac arrhythmias by combining body surface electrocardiographic (ECG) mapping (BSM) and imaging. MATERIALS AND METHODS: This study was approved by the institutional review board, and all patients gave written informed consent. Twenty-seven patients referred for electrophysiologic procedures in the context of ventricular tachycardia (VT) (n = 9), Wolff-Parkinson-White (WPW) syndrome (n = 2), atrial fibrillation (AF) (n = 13), or scar-related ventricular fibrillation (VF) (n = 3) were examined. Patients underwent BSM and imaging with multidetector computed tomography (CT) (n = 12) and/or delayed enhanced magnetic resonance (MR) imaging (n = 23). BSM was performed by using a 252-electrode vest that enabled the computation of epicardial electrograms from body surface potentials. The epicardial geometry used for BSM was registered to the epicardial geometry segmented from imaging data by using an automatic algorithm. The output was a three-dimensional cardiac model that integrated cardiac anatomy, myocardial substrate, and epicardial activation. RESULTS: Acquisition, segmentation, and registration were feasible in all patients. In VT, this enabled a noninvasive assessment of the arrhythmia mechanism and its location with respect to the myocardial substrate, coronary vessels, and phrenic nerve. In WPW syndrome, this enabled understanding of complex accessory pathways resistant to previous ablation. In AF and VF, this enabled the noninvasive assessment of arrhythmia mechanisms and the analysis of rotor trajectories with respect to the myocardial substrate. In all patients, models were successfully integrated in navigation systems and used to guide mapping and ablation. CONCLUSION: By combining information on anatomy, substrate, and electrical activation, the fusion of BSM and imaging enables comprehensive noninvasive assessment of cardiac arrhythmias, with potential applications for diagnosis, prognosis, and ablation targeting. Online supplemental material is available for this article.

Mutual Information Guided Diffusion for Zero-shot Cross-modality Medical Image Translation.

Cross-modality data translation has attracted great interest in medical image computing. Deep generative models show performance improvement in addressing related challenges. Nevertheless, as a fundamental challenge in image translation, the problem of zero-shot learning cross-modality image translation with fidelity remains unanswered. To bridge this gap, we propose a novel unsupervised zero-shot learning method called Mutual Information guided Diffusion Model, which learns to translate an unseen source image to the target modality by leveraging the inherent statistical consistency of Mutual Information between different modalities. To overcome the prohibitive high dimensional Mutual Information calculation, we propose a differentiable local-wise mutual information layer for conditioning the iterative denoising process. The Local-wise-Mutual-Information-Layer captures identical cross-modality features in the statistical domain, offering diffusion guidance without relying on direct mappings between the source and target domains. This advantage allows our method to adapt to changing source domains without the need for retraining, making it highly practical when sufficient labeled source domain data is not available. We demonstrate the superior performance of MIDiffusion in zero-shot cross-modality translation tasks through empirical comparisons with other generative models, including adversarial-based and diffusion-based models. Finally, we showcase the real-world application of MIDiffusion in 3D zero-shot learning-based cross-modality image segmentation tasks.

The role of the pulmonary veins on left atrial flow patterns and thrombus formation.

Atrial fibrillation (AF) is the most common human arrhythmia, forming thrombi mostly in the left atrial appendage (LAA). However, the relation between LAA morphology, blood patterns and clot formation is not yet fully understood. Furthermore, the impact of anatomical structures like the pulmonary veins (PVs) have not been thoroughly studied due to data acquisition difficulties. In-silico studies with flow simulations provide a detailed analysis of blood flow patterns under different boundary conditions, but a limited number of cases have been reported in the literature. To address these gaps, we investigated the influence of PVs on LA blood flow patterns and thrombus formation risk through computational fluid dynamics simulations conducted on a sizeable cohort of 130 patients, establishing the largest cohort of patient-specific LA fluid simulations reported to date. The investigation encompassed an in-depth analysis of several parameters, including pulmonary vein orientation (e.g., angles) and configuration (e.g., number), LAA and LA volumes as well as their ratio, flow, and mass-less particles. Our findings highlight the total number of particles within the LAA as a key parameter for distinguishing between the thrombus and non-thrombus groups. Moreover, the angles between the different PVs play an important role to determine the flow going inside the LAA and consequently the risk of thrombus formation. The alignment between the LAA and the main direction of the left superior pulmonary vein, or the position of the right pulmonary vein when it exhibits greater inclination, had an impact to distinguish the control group vs. the thrombus group. These insights shed light on the intricate relationship between PV configuration, LAA morphology, and thrombus formation, underscoring the importance of comprehensive blood flow pattern analyses.

Integration of merged delayed-enhanced magnetic resonance imaging and multidetector computed tomography for the guidance of ventricular tachycardia ablation: a pilot study.

BACKGROUND: Delayed enhancement (DE) MRI can assess the fibrotic substrate of scar-related VT. MDCT has the advantage of inframillimetric spatial resolution and better 3D reconstructions. We sought to evaluate the feasibility and usefulness of integrating merged MDCT/MRI data in 3D-mapping systems for structure-function assessment and multimodal guidance of VT mapping and ablation. METHODS: Nine patients, including 3 ischemic cardiomyopathy (ICM), 3 nonischemic cardiomyopathy (NICM), 2 myocarditis, and 1 redo procedure for idiopathic VT, underwent MRI and MDCT before VT ablation. Merged MRI/MDCT data were integrated in 3D-mapping systems and registered to high-density endocardial and epicardial maps. Low-voltage areas (<1.5 mV) and local abnormal ventricular activities (LAVA) during sinus rhythm were correlated to DE at MRI, and wall-thinning (WT) at MDCT. RESULTS: Endocardium and epicardium were mapped with 391 ± 388 and 1098 ± 734 points per map, respectively. Registration of MDCT allowed visualization of coronary arteries during epicardial mapping/ablation. In the idiopathic patient, integration of MRI data identified previously ablated regions. In ICM patients, both DE at MRI and WT at MDCT matched areas of low voltage (overlap 94 ± 6% and 79 ± 5%, respectively). In NICM patients, wall-thinning areas matched areas of low voltage (overlap 63 ± 21%). In patients with myocarditis, subepicardial DE matched areas of epicardial low voltage (overlap 92 ± 12%). A total number of 266 LAVA sites were found in 7/9 patients. All LAVA sites were associated to structural substrate at imaging (90% inside, 100% within 18 mm). CONCLUSION: The integration of merged MDCT and DEMRI data is feasible and allows combining substrate assessment with high-spatial resolution to better define structure-function relationship in scar-related VT.

Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning.

Modelling complex systems, like the human heart, has made great progress over the last decades. Patient-specific models, called 'digital twins', can aid in diagnosing arrhythmias and personalizing treatments. However, building highly accurate predictive heart models requires a delicate balance between mathematical complexity, parameterization from measurements and validation of predictions. Cardiac electrophysiology (EP) models range from complex biophysical models to simplified phenomenological models. Complex models are accurate but computationally intensive and challenging to parameterize, while simplified models are computationally efficient but less realistic. In this paper, we propose a hybrid approach by leveraging deep learning to complete a simplified cardiac model from data. Our novel framework has two components, decomposing the dynamics into a physics based and a data-driven term. This construction allows our framework to learn from data of different complexity, while simultaneously estimating model parameters. First, using in silico data, we demonstrate that this framework can reproduce the complex dynamics of cardiac transmembrane potential even in the presence of noise in the data. Second, using ex vivo optical data of action potentials (APs), we demonstrate that our framework can identify key physical parameters for anatomical zones with different electrical properties, as well as to reproduce the AP wave characteristics obtained from various pacing locations. Our physics-based data-driven approach may improve cardiac EP modelling by providing a robust biophysical tool for predictions.