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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).

Aims Left atrial (LA) fibrosis is present in patients with atrial fibrillation (AF) and can be visualized by magnetic resonance imaging with late gadolinium enhancement (LGE-MRI). Previous studies have shown that LA fibrosis is not randomly distributed, being more frequent in the area adjacent to the descending aorta (DAo). The objective of this study is to analyse the relationship between fibrosis in the atrial area adjacent to the DAo and the distance to it, as well as the prognostic implications of this fibrosis. Methods and results Magnetic resonance imaging with late gadolinium enhancement was obtained in 108 patients before AF ablation to analyse the extent of LA fibrosis and the distance DAo-to-LA. A high-density electroanatomic map was performed in a subgroup of 16 patients to exclude the possibility of an MRI artifact. Recurrences after ablation were analysed at 1 year of follow-up. The extent of atrial fibrosis in the area adjacent to the DAo was inversely correlated with the distance DAo-to-LA (r = −0.34, P Conclusions Atrial fibrosis was predominantly located in the area adjacent to the DAo, and increased with the proximity between the two structures. Furthermore, this regional fibrosis better predicted recurrence after AF ablation than total atrial fibrosis.

International audience; The short-axis view defined such that a series of slices are perpendicular to the long-axis of the left ventricle (LV) is one of the most important views in cardiovascular imaging. Raw trans-axial Computed Tomography (CT) images must be often reformatted prior to diagnostic interpretation in short-axis view. The clinical importance of this refor-matting requires the process to be accurate and reproducible. It is often performed after manual localization of landmarks on the image (e.g. LV apex, centre of the mitral valve, etc.) being slower and not fully reproducible as compared to automatic approaches. We propose a fast, automatic and reproducible method to reformat CT images from original trans-axial orientation to short-axis view. A deep learning based seg-mentation method is used to automatically segment the LV endocardium and wall, and the right ventricle epicardium. Surface meshes are then obtained from the corresponding masks and used to automatically detect the shape features needed to find the transformation that locates the cardiac chambers on their standard, mathematically defined, short-axis position. 25 datasets with available manual reformatting performed by experienced cardiac radiologists are used to show that our reformatted images are of equivalent quality.

Two-dimensional representation of 3D anatomical structures is a simple and intuitive way for analysing patient information across populations and image modalities. While cardiac ventricles, especially the left ventricle, have an established standard representation (bull's eye plot), the 2D depiction of the left atrium (LA) remains challenging due to its sub-structural complexity including the pulmonary veins (PV) and the left atrial appendage (LAA). Quasi-conformal flattening techniques, successfully applied to cardiac ventricles, require additional constraints in the case of the LA to place the PV and LAA in the same geometrical 2D location for different cases. Some registration-based methods have been proposed but surface registration is time-consuming and prone to errors when the geometries are very different. We propose a novel atrial flattening methodology where a 2D standardised map of the LA is obtained quickly and without errors related to registration. The LA is divided into five regions which are then mapped to their analogue two-dimensional regions. 67 human left atria from magnetic resonance images (MRI) were studied to derive a population-based template representing the averaged relative locations of the PVs and LAA. The clinical application of our methodology is illustrated on different use cases including the integration of MRI and electroanatomical data. This study was partially funded by the Spanish Ministry of Economy and Competitiveness (DPI2015-71640-R), by the “Fundació La Marató de TV3” (no20154031) and by European Union Horizon 2020 Programme for Research and Innovation, under grant agreement No. 642676 (CardioFunXion).

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.

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 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.

Comunicació presentada a la 9th international conference on Functional Imaging and Modeling of the Heart (FIMH 2017), celebrada els dies 11 a 13 de juny de 2017 a Toronto, Canadà. Many cardiac diseases are associated with changes in ventricular shape. However, in daily practice, the heart is mostly assessed by 2D echocardiography only. While 3D techniques are available, they are rarely used. In this paper we analyze to which extent it is possible to obtain the 3D shape of a left ventricle (LV) using measurements from 2D echocardiography. First, we investigate this using synthetic datasets, and afterwards, we illustrate it in clinical 2D echocardiography measurements with corresponding 3D meshes obtained using 3D echocardiography. We demonstrate that standard measurements taken in 2D allow quantifying only the ellipsoidal shape of the ventricle, and that capturing other shape features require either additional geometrical measurements or clinical information related to shape remodelling. We show that noise in the measurements is the primary cause for poor association between the measurements and the LV shape features and that an estimated 10% level of noise on the 2D measurements limits the recoverability of shape. Finally we show that clinical variables relating to the clinical history can substitute the lack of geometric measurements, thus providing alternatives for shape assessment in daily practice. This study was partially supported by the Spanish Ministry of Economy and Competitiveness (grant TIN2014-52923-R; Maria de Maeztu Units of Excellence Programme - MDM-2015-0502), FEDER and the European Union Horizon 2020 Programme for Research and Innovation, under grant agreement No. 642676 (CardioFunXion).

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 aspects related to the pathogenesis and progression of atrial fibrillation (AF) are still not fully understood. In this thesis we have contributed with (1) an automatic framework to segment the left atrium (LA) from late gadolinium enhancement magnetic resonance imaging (LGE-MRI) data, (2) a framework for obtaining a standardised two-dimensional representation of the left atrial cavity, and (3) a methodology to detect and measure the incompleteness of ablation lesions after pulmonary vein isolation (PVI). Finally, we applied our tools to real clinical datasets showing how our methods can be transferred and included into clinical research practice. We investigated the regional distribution of gaps in a cohort of AF patients who undergone radiofrequency PVI, the reproducibility of scar imaging with LGE-MRI, the intra- and inter-observer variability of manual LA segmentation tools, the preferential regional distribution of fibrosis in AF patients, and the relation between electroanatomical information and LGE-MRI data. Muchos aspectos relacionados con la patogénesis y progresión de la fibrilación auricular (FA) son todavía desconocidos. En esta tesis, hemos propuesto (1) un método para segmentar la aurícula izquierda (AI) en datos de resonancia magnética con realce tardío por gadolinio (LGE-MRI), (2) un framework para obtener una representación bidimensional estandarizada de la aurícula izquierda, y (3) una técnica para detectar y medir la incompletitud (gaps) de las lesiones de ablación después del aislamiento de las venas pulmonares (PVI). Finalmente, hemos aplicado nuestras herramientas a datos clínicos reales mostrando cómo nuestros métodos pueden transferirse e incluirse en investigación clínica. Concretamente, hemos investigado la distribución regional de gaps en pacientes con FA, la reproducibilidad de detección de cicatriz con LGE-MRI, la variabilidad intra e inter-observador de las herramientas de segmentación manual de AI, la distribución regional de fibrosis en pacientes con FA, y la relación entre la información electroanatómica y LGE-MRI.