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.

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.

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.

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.

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.

Additional prognostic value of echocardiographic follow-up in pulmonary hypertension-role of 3D right ventricular area strain.

AIMS: Outcomes in pulmonary hypertension (PH) are related to right ventricular (RV) function and remodelling. We hypothesized that changes in RV function and especially area strain (AS) could provide incremental prognostic information compared to the use of baseline data only. We therefore aimed to assess RV function changes between baseline and 6-month follow-up and evaluate their prognostic value for PH patients using 3D echocardiography. METHODS AND RESULTS: Ninety-five PH patients underwent a prospective longitudinal study including ESC/ERS guidelines prognostic assessment and 3D RV echocardiographic imaging at baseline and 6-month follow-up. Semi-automatic software tracked the RV along the cycle, and its output was post-processed to extract 3D deformation patterns. Over a median follow-up of 24.8 (22.1-25.7) months, 21 patients died from PH or were transplanted. Improvements in RV global AS were associated with stable or improving clinical condition as well as survival free from transplant (P < 0.001). The 3D deformation patterns confirmed that the most significant regional changes occurred within the septum. RV global AS change over 6-month by +3.5% identifies patients with a 3.7-fold increased risk of death or transplant. On multivariate COX analysis, changes in WHO class, BNP, and RV global AS were independent predictors of outcomes. Besides, the combination of these three parameters was of special interest to identify high-risk patients [HR 11.5 (1.55-86.06)]. CONCLUSION: Changes in RV function and especially changes in 3D RV AS are of prognostic importance. Our study underlines that assessing such changes from baseline to follow-up is of additional prognostic value for PH patients. CLINICAL TRIAL REGISTRATION: http://clinicaltrials.gov/ct2/show/NCT02799979.

Improved myocardial scar visualization with fast free-breathing motion-compensated black-blood T1-rho-prepared late gadolinium enhancement MRI.

PURPOSE: Clinical guidelines recommend the use of bright-blood late gadolinium enhancement (BR-LGE) for the detection and quantification of regional myocardial fibrosis and scar. This technique, however, may suffer from poor contrast at the blood-scar interface, particularly in patients with subendocardial myocardial infarction. The purpose of this study was to assess the clinical performance of a two-dimensional black-blood LGE (BL-LGE) sequence, which combines free-breathing T1-rho-prepared single-shot acquisitions with an advanced non-rigid motion-compensated patch-based reconstruction. MATERIALS AND METHODS: Extended phase graph simulations and phantom experiments were performed to investigate the performance of the motion-correction algorithm and to assess the black-blood properties of the proposed sequence. Fifty-one patients (37 men, 14 women; mean age, 55 ± 15 [SD] years; age range: 19-81 years) with known or suspected cardiac disease prospectively underwent free-breathing T1-rho-prepared BL-LGE imaging with inline non-rigid motion-compensated patch-based reconstruction at 1.5T. Conventional breath-held BR-LGE images were acquired for comparison purposes. Acquisition times were recorded. Two readers graded the image quality and relative contrasts were calculated. Presence, location, and extent of LGE were evaluated. RESULTS: BL-LGE images were acquired with full ventricular coverage in 115 ± 25 (SD) sec (range: 64-160 sec). Image quality was significantly higher on free-breathing BL-LGE imaging than on its breath-held BR-LGE counterpart (3.6 ± 0.7 [SD] [range: 2-4] vs. 3.9 ± 0.2 [SD] [range: 3-4]) (P <0.01) and was graded as diagnostic for 44/51 (86%) patients. The mean scar-to-myocardium and scar-to-blood relative contrasts were significantly higher on BL-LGE images (P < 0.01 for both). The extent of LGE was larger on BL-LGE (median, 5 segments [IQR: 2, 7 segments] vs. median, 4 segments [IQR: 1, 6 segments]) (P < 0.01), the method being particularly sensitive in segments with LGE involving the subendocardium or papillary muscles. In eight patients (16%), BL-LGE could ascertain or rule out a diagnosis otherwise inconclusive on BR-LGE. CONCLUSION: Free-breathing T1-rho-prepared BL-LGE imaging with inline motion compensated reconstruction offers a promising diagnostic technology for the non-invasive assessment of myocardial injuries.

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge.

Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/).

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.

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.

Biophysics-based statistical learning: Application to heart and brain interactions.

Initiatives such as the UK Biobank provide joint cardiac and brain imaging information for thousands of individuals, representing a unique opportunity to study the relationship between heart and brain. Most of research on large multimodal databases has been focusing on studying the associations among the available measurements by means of univariate and multivariate association models. However, these approaches do not provide insights about the underlying mechanisms and are often hampered by the lack of prior knowledge on the physiological relationships between measurements. For instance, important indices of the cardiovascular function, such as cardiac contractility, cannot be measured in-vivo. While these non-observable parameters can be estimated by means of biophysical models, their personalisation is generally an ill-posed problem, often lacking critical data and only applied to small datasets. Therefore, to jointly study brain and heart, we propose an approach in which the parameter personalisation of a lumped cardiovascular model is constrained by the statistical relationships observed between model parameters and brain-volumetric indices extracted from imaging, i.e. ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We explored the plausibility of the learnt relationships by inferring the model parameters conditioned on the absence of part of the target clinical features, applying this framework in a cohort of more than 3 000 subjects and in a pathological subgroup of 59 subjects diagnosed with atrial fibrillation. Our results demonstrate the impact of such external features in the cardiovascular model personalisation by learning more informative parameter-space constraints. Moreover, physiologically plausible mechanisms are captured through these personalised models as well as significant differences associated to specific clinical conditions.

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.

Applications of artificial intelligence in cardiovascular imaging.

Research into artificial intelligence (AI) has made tremendous progress over the past decade. In particular, the AI-powered analysis of images and signals has reached human-level performance in many applications owing to the efficiency of modern machine learning methods, in particular deep learning using convolutional neural networks. Research into the application of AI to medical imaging is now very active, especially in the field of cardiovascular imaging because of the challenges associated with acquiring and analysing images of this dynamic organ. In this Review, we discuss the clinical questions in cardiovascular imaging that AI can be used to address and the principal methodological AI approaches that have been developed to solve the related image analysis problems. Some approaches are purely data-driven and rely mainly on statistical associations, whereas others integrate anatomical and physiological information through additional statistical, geometric and biophysical models of the human heart. In a structured manner, we provide representative examples of each of these approaches, with particular attention to the underlying computational imaging challenges. Finally, we discuss the remaining limitations of AI approaches in cardiovascular imaging (such as generalizability and explainability) and how they can be overcome.

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.

Left atrial shape is independent predictor of arrhythmia recurrence after catheter ablation for atrial fibrillation: A shape statistics study.

BACKGROUND: Markers of left atrial (LA) shape may improve the prediction of postablation outcomes in atrial fibrillation (AF). Correlations to LA volume and AF persistence limit their incremental value over current clinical predictors. OBJECTIVE: To develop a shape score independent from AF persistence and LA volume using shape-based statistics, and to test its ability to predict postablation outcome. METHODS: Preablation computed tomography (CT) images from 141 patients with paroxysmal (57%) or persistent (43%) AF were segmented. Deformation of an average LA shape into each patient encoded patient-specific shape. Local analysis investigates regional differences between patient groups. Linear regression was used to remove shape variations related to LA volume and AF persistence, and to build a shape score to predict postablation outcome. Cross-validation was performed to evaluate its accuracy. RESULTS: Ablation failure rate was 23% over a median 12-month follow-up. Regions associated with ablation failure mostly consisted of a large area on posteroinferior LA, mitral isthmus, and left inferior vein. On univariate analysis, strongest predictors were AF persistence (P = .005), LA indexed volume (P = .02), and the proposed shape score (P = .001). On multivariate analysis, all 3 were independent predictors of ablation failure, with the LA shape score showing the highest predictive value (odds ratio [OR] = 6.2 [2.5-15.8], P < .001), followed by LA indexed volume (OR = 3.1 [1.2-7.9], P = .019) and AF persistence (OR = 2.9 [1.2-7.6], P = .022). CONCLUSION: Posteroinferior LA, mitral isthmus, and left inferior vein are the regions whose shape have the highest impact on outcome. LA shape predicts AF ablation failure independently from, and more accurately than, atrial volume and AF persistence.

Three-dimensional right ventricular shape and strain in congenital heart disease patients with right ventricular chronic volume loading.

AIMS: Right ventricular (RV) function assessment is crucial in congenital heart disease patients, especially in atrial septal defect (ASD) and repaired Tetralogy of Fallot (TOF) patients with pulmonary regurgitation (PR). In this study, we aimed to analyse both 3D RV shape and deformation to better characterize RV function in ASD and TOF-PR. METHODS AND RESULTS: We prospectively included 110 patients (≥16 years old) into this case-control study: 27 ASD patients, 28 with TOF, and 55 sex- and age-matched healthy controls. Endocardial tracking was performed on 3D transthoracic RV echocardiographic sequences and output RV meshes were post-processed to extract local curvature and deformation. Differences in shape and deformation patterns between subgroups were quantified both globally and locally. Curvature highlights differences in RV shape between controls and patients while ASD and TOF-PR patients are similar. Conversely, strain highlights differences between controls and TOF-PR patients while ASD and controls are similar [global area strain: -31.5 ± 5.8% (controls), -34.1 ± 7.9% (ASD), -24.8 ± 5.7% (TOF-PR), P < 0.001, similar significance for longitudinal and circumferential strains]. The regional and local analysis highlighted differences in particular in the RV free wall and the apical septum. CONCLUSION: Chronic RV volume loading results in similar RV shape remodelling in both ASD and TOF patients while strain analysis demonstrated that RV strain is only reduced in the TOF group. This suggests a fundamentally different RV remodelling process between both conditions.