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Aims Myocardial fibrosis is a hallmark of atrial fibrillation (AF) and its characterization could be used to guide ablation procedures. Late gadolinium enhanced-magnetic resonance imaging (LGE-MRI) detects areas of atrial fibrosis. However, its accuracy remains controversial. We aimed to analyse the accuracy of LGE-MRI to identify left atrial (LA) arrhythmogenic substrate by analysing voltage and conduction velocity at the areas of LGE. Methods and results Late gadolinium enhanced-magnetic resonance imaging was performed before ablation in 16 patients. Atrial wall intensity was normalized to blood pool and classified as healthy, interstitial fibrosis, and dense scar tissue depending of the resulting image intensity ratio. Bipolar voltage and local conduction velocity were measured in LA with high-density electroanatomic maps recorded in sinus rhythm and subsequently projected into the LGE-MRI. A semi-automatic, point-by-point correlation was made between LGE-MRI and electroanatomical mapping. Mean bipolar voltage and local velocity progressively decreased from healthy to interstitial fibrosis to scar. There was a significant negative correlation between LGE with voltage (r = −0.39, P Conclusions Areas with higher LGE show lower voltage and slower conduction in sinus rhythm. The enhancement intensity correlates with bipolar voltage and conduction velocity in a point-by-point analysis. The performance of LGE-MRI in assessing local velocity might be reduced in patients with dilated atria (LA diameter ≥45).

The aim of this study is to create an automatic framework for sustained ventricular arrhythmia (VA) prediction using cardiac computed tomography (CT) images. We built an image processing pipeline and a deep learning network to explore the relation between post-infarct left ventricular myocardium thickness and previous occurrence of VA. Our pipeline generated a 2D myocardium thickness map (TM) from the 3D imaging input. Our network consisted of a conditional variational autoencoder (CVAE) and a classifier model. The CVAE was used to compress the TM into a low dimensional latent space, then the classifier utilised the latent variables to predict between healthy and VA patient. We studied the network on a large clinical database of 504 healthy and 182 VA patients. Using our method, we achieved a mean classification accuracy of \(75\% \pm 4\) on the testing dataset, compared to \(71\% \pm 4\) from the classification using the classical left ventricular ejection fraction (LVEF).

Atrial fibrillation (AF) is nowadays the most common human arrhythmia and it is considered a marker of an increased risk of embolic stroke. It is known that 99% of AF-related thrombi are generated in the left atrial appendage (LAA), an anatomical structure located within the left atrium (LA). Left atrial appendage occlusion (LAAO) has become a good alternative for nonvalvular AF patients with contraindications to anticoagulants. However, there is a non-negligible number of device-related thrombus (DRT) events, created next to the device surface. In silico fluid simulations can be a powerful tool to better understand the relation between LA anatomy, haemodynamics, and the process of thrombus formation. Despite the increasing literature in LA fluid modelling, a consensus has not been reached yet in the community on the optimal modelling choices and boundary conditions for generating realistic simulations. In this line, we have performed a sensitivity analysis of several boundary conditions scenarios, varying inlet/outlet and LA wall movement configurations, using patient-specific imaging data of six LAAO patients (three of them with DRT at follow-up). Mesh and cardiac cycle convergence were also analysed. The boundary conditions scenario that better predicted DRT cases had echocardiography-based velocities at the mitral valve outlet, a generic pressure wave from an AF patient at the pulmonary vein inlets, and a dynamic mesh approach for LA wall deformation, emphasizing the need for patient-specific data for realistic simulations. The obtained promising results need to be further validated with larger cohorts, ideally with ground truth data, but they already offer unique insights on thrombogenic risk in the left atria. This work was supported by the Spanish Ministry of Science, Innovation and Universities under the Retos I+D Programme (RTI2018-101193-B-I00), the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), the Spanish Ministry of Economy and Competitiveness under the Programme for the Formation of Doctors (PRE2018-084062), and MINECO (RYC-2015-18888). Additionally, this work was supported by the H2020 EU SimCardioTest project (Digital transformation in Health and Care SC1-DTH-06-2020; grant agreement No. 101016496).

Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community. (C) 2020 Elsevier B.V. All rights reserved.

AIMS: Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) may define left atrial (LA) anatomy and structural remodelling, and facilitate atrial fibrillation (AF) ablation. We aimed to assess the intra- and inter-observer reproducibility and agreement of LGE-CMR parameters with direct application to AF ablation techniques. METHODS AND RESULTS: One experienced and one non-experienced observer performed complete LGE-CMR data analysis twice, on different days, in 40 randomly selected LGE-CMR examinations [20 performed before ablation (pre-ablation) and 20 performed 3 months after ablation (post-ablation)]. Four additional observers (two experienced and two non-experienced) performed complete LGE-CMR data analysis in a subgroup of 30 patients (15 pre-ablation and 15 post-ablation). All LGE-CMR were performed in sinus rhythm. Intra- and inter-observer reproducibility of LA volume, LA area, and sphericity index (SI) was high: coefficient of variation 0.71. Geometric congruency of repeated reconstruction of LA shape was high: maximal error 0.75) when the scar area was >5 cm2 for a single observer and >15 cm2 for multiple observers. Non-experienced observers performed equally well to experienced observers. CONCLUSION: Late gadolinium enhancement cardiac magnetic resonance measurements of LA area, volume, and SI were reproducible, and geometric congruency of LA shape was high. Location of scar was precise for scar areas >5 cm2 for single observers and >15 cm2 for multiple observers, regardless of the observers' experience. These results may serve as a reference for future studies on the role for substrate-based AF ablation procedures.

The majority of thrombi in non-valvular atrial fibrillation (AF) patients are formed in the left atrial appendage (LAA). The shape of the LAA is highly variable and complex morphologies seem to favour the development of thrombus since they may induce blood stasis. Nevertheless, the relation between LAA shape and risk of thrombus has not been rigorously studied due to the lack of appropriate imaging data and robust tools to characterize LAA morphology. The main goal of this work was to automatically extract simple LAA morphological descriptors and study their relation with the presence of thrombus. LAA shape characterization was based on the computation of its centreline combining a heat transfer-derived distance map and a marching algorithm, once it LAA was segmented from 3D medical images. From the LAA centreline, several morphological descriptors were derived such as its length, tortuosity and major bending angles, among others. Other LAA parameters such as surface area, volume and ostium shape parameters were also obtained. A total of 71 LAA geometries from AF patients were analysed; 33 of them with a history of a thromboembolic event. We performed a statistical analysis to identify morphological descriptors showing differences between patients with and without thrombus history. Ostium size and centreline length were significatively different in both cohorts, with larger average values in thromboembolic cases, which could be related to slower blood flow velocities within the LAA. Additionally, some of the obtained centreline-based LAA parameters could be used for a better planning of LAA occluder implantations.

The left atrial appendage (LAA) is a complex and heterogeneous bulge structure of the left atrium (LA). It is known that, in atrial fibrillation (AF) patients, around 70% to 90% of the thrombi are formed there. However, the exact mechanism of the process of thrombus formation and the role of the LAA in that process are not fully understood yet. The main goal of this work is to perform patient-specific haemodynamics simulations of the LA and LAA and jointly analyse the resulting blood flow parameters with morphological descriptors of these structures in relation with the risk of thrombus formation. Some LAA morphological descriptors such as ostium characteristics and pulmonary configuration were found to influence LAA blood flow patterns. These findings improve our knowledge on the required conditions for thrombus formation in the LAA.

Aims: Left atrial (LA) fibrosis can be identified by late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) in patients with atrial fibrillation (AF). However, there is limited information about anatomical fibrosis distribution in the left atrium. The aim is to determine whether there is a preferential spatial distribution of fibrosis in the left atrium in patients with AF. Methods and results: A 3-Tesla LGE-CMR was performed in 113 consecutive patients referred for AF ablation. Images were post-processed and analysed using ADAS-AF software (Galgo Medical), which allows fibrosis identification in 3D colour-coded shells. A regional semiautomatic LA parcellation software was used to divide the atrial wall into 12 segments: 1–4, posterior wall; 5–6, floor; 7, septal wall; 8–11, anterior wall; 12, lateral wall. The presence and amount of fibrosis in each segment was obtained for analysis. After exclusions for artefacts and insufficient image quality, 76 LGE-MRI images (68%) were suitable for fibrosis analysis. Segments 3 and 5, closest to the left inferior pulmonary vein, had significantly higher fibrosis (40.42% ± 23.96 and 25.82% ± 21.24, respectively; P 60 years was significantly associated with increased LA fibrosis [95% confidence interval (CI) 0.19–8.39, P = 0.04] and persistent AF approached significance (95% CI −0.19% to 7.83%, P = 0.08). Conclusion: In patients with AF, the fibrotic area is preferentially located at the posterior wall and floor around the antrum of the left inferior pulmonary vein. Age >60 years was associated with increased fibrosis. This work was partially supported by Fondo de Investigaciones Sanitarias-Instituto de Salud Carlos III (PI13/01747 and PI16/00435); Agencia de gestió d´Ajuts Universitaris I de Recerca (AGAUR) Exp: 2014_SGR_471; CERCA programme/Generalitat de Catalunya; European Regional Development Fund (ERDF. European Union. A Way of Making Europe), the European Union's Horizon 2020 research and innovation programme under grant agreement No 633196 (CATCH ME), a grant by La MARATÓ-TV3 (20152730) and the Spanish Ministry of Economy and Competitiveness (DPI2015-71640-R).

The influence of the left atrial appendage (LAA) and its different possible morphologies in atrial haemodynamics and thrombus formation is not fully known yet. The main goal of this work is to analyse blood flow characteristics in relation with LA/LAA morphologies to better understand conditions that may lead to thrombus formation. We constructed several patient-specific computational meshes of left atrial geometries from medical imaging data. Subsequently, Computational Fluid Dynamics (CFD) methods were run with boundary conditions based on pressure and velocity measurements from literature. Relevant indices characterizing the simulated flows such as local maps of vorticity were related to simple LAA shape parameters. Our in silico study provided different 3D haemodynamics patterns dependent on the patient-specific atrial geometry. It also suggests that areas near the LAA ostium and with presence of lobes are more prone to coagulation due to the presence of low velocities and vortices.

The left atrial appendage (LAA) is a complex and heterogeneous protruding structure of the left atrium (LA). In atrial fibrillation patients, it is the location where 90% of the thrombi are formed. However, the role of the LAA in thrombus formation is not fully known yet. The main goal of this work is to perform a sensitivity analysis to identify the most relevant LA and LAA morphological parameters in atrial blood flow dynamics. Simulations were run on synthetic ellipsoidal left atria models where different parameters were individually studied: pulmonary veins and mitral valve dimensions; LAA shape; and LA volume. Our computational analysis confirmed the relation between large LAA ostia, low blood flow velocities and thrombus formation. Additionally, we found that pulmonary vein configuration exerted a critical influence on LAA blood flow patterns. These findings contribute to a better understanding of the LAA and to support clinical decisions for atrial fibrillation patients.

Background— Fetal growth restriction (FGR) affects 5% to 10% of newborns and is associated with increased cardiovascular mortality in adulthood. We evaluated whether prenatal cardiovascular changes previously demonstrated in FGR persist into preadolescence. Methods and Results— A cohort study of 58 FGR (defined as birth weight below 10th centile) and 94 normally grown fetuses identified in utero and followed-up into preadolescence (8–12 years of age) by echocardiography and 3-dimensional shape computational analysis. Compared with controls, FGR preadolescents had a different cardiac shape, with more spherical and smaller hearts. Left ventricular ejection fraction was similar among groups, whereas FGR had decreased longitudinal motion (decreased mitral annular systolic peak velocities: control median, 0.11 m/s [interquartile range, 0.09–0.12] versus FGR median 0.09 m/s [interquartile range, 0.09–0.10]; P P =0.04). Global longitudinal strain was decreased (control mean, −22.4% [SD, 1.37] versus FGR mean, −21.5% [SD, 1.16]; P Conclusions— This study provides evidence that cardiac remodeling induced by FGR persists until preadolescence with findings similar to those reported in their prenatal life and childhood. The findings support the hypothesis of primary cardiac programming in FGR for explaining the association between low birth weight and cardiovascular risk in adulthood.

Many uncertainties remain about the relation between post-infarct scars and ventricular arrhythmia. Most post-infarct patients suffer scar-related arrhythmia several years after the infarct event suggesting that scar remodeling is a process that might require years until the affected tissue becomes arrhythmogenic. In clinical practice, a simple time-based rule is often used to assess risk and stratify patients. In other cases, left ventricular ejection fraction (LVEF) impairment is also taken into account but it is known to be suboptimal. More information is needed to better stratify patients and prescribe appropriate individualized treatments. In this paper we propose to use probabilistic disease progression modeling to obtain an image-based data-driven description of the infarct maturation process. Our approach includes monotonic constraints in order to impose a regular behaviour on the biomarkers’ trajectories. 49 post-MI patients underwent Computed Tomography (CT) and Late Gadolinium Enhanced Cardiac Magnetic Resonance (LGE-CMR) scans. Image-derived biomarkers were computed such as LVEF, LGE-CMR scar volume, fat volume, and size of areas with a different degree of left ventricular wall narrowing, from moderate to severe. We show that the model is able to estimate a plausible progression of post-infarct scar maturation. According to our results there is a progressive thinning process observable only with CT imaging; intramural fat appears in a late stage; LGE-CMR scar volume almost does not change and LVEF slightly changes during the scar maturation process.