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.

Cation Exchange Enabled Cu Dopants Location Tailoring and Photoelectric Properties Regulation in CdS Nanosheets.

Doping-related point defect engineering in low-dimensional semiconductor nanostructures is important to regulate their optical and electronic properties. The substitutional or interstitial location of heterovalent dopants is critical and has not been controlled effectively yet. Herein, we carefully control the kinetics of reverse cation exchange between CuxS 2D nanosheets and ligand-coordinated Cd2+ cations to control the Cu doping sites in CdS nanosheets (NSs). The substitutional and interstitial Cu dopants were directly confirmed by spherical aberration-corrected TEM (SACTEM) and their X-ray absorption spectroscopy (XAS) coordination investigation. Density functional theory (DFT) calculations and their experimental conductivities and dopant luminescence performance demonstrated the dramatic differences that are due to the location of different Cu dopants. These findings provide deeper insights on dopants' location regulation in a nanostructured host semiconductor.

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.

A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

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.

Algorithms for left atrial wall segmentation and thickness - Evaluation on an open-source CT and MRI image database.

Structural changes to the wall of the left atrium are known to occur with conditions that predispose to Atrial fibrillation. Imaging studies have demonstrated that these changes may be detected non-invasively. An important indicator of this structural change is the wall's thickness. Present studies have commonly measured the wall thickness at few discrete locations. Dense measurements with computer algorithms may be possible on cardiac scans of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The task is challenging as the atrial wall is a thin tissue and the imaging resolution is a limiting factor. It is unclear how accurate algorithms may get and how they compare in this new emerging area. We approached this problem of comparability with the Segmentation of Left Atrial Wall for Thickness (SLAWT) challenge organised in conjunction with MICCAI 2016 conference. This manuscript presents the algorithms that had participated and evaluation strategies for comparing them on the challenge image database that is now open-source. The image database consisted of cardiac CT (n=10) and MRI (n=10) of healthy and diseased subjects. A total of 6 algorithms were evaluated with different metrics, with 3 algorithms in each modality. Segmentation of the wall with algorithms was found to be feasible in both modalities. There was generally a lack of accuracy in the algorithms and inter-rater differences showed that algorithms could do better. Benchmarks were determined and algorithms were ranked to allow future algorithms to be ranked alongside the state-of-the-art techniques presented in this work. A mean atlas was also constructed from both modalities to illustrate the variation in thickness within this small cohort.