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.

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.

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.

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.

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.

Improved myocardial scar visualization with fast free-breathing motion-compensated black-blood T-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 T-rho-prepared single-shot acquisitions with an advanced non-rigid motion-compensated patch-based reconstruction.

Decoding Genetic Markers of Multiple Phenotypic Layers Through Biologically Constrained Genome-To-Phenome Bayesian Sparse Regression.

The applicability of multivariate approaches for the joint analysis of genomics and phenomics information is currently limited by the lack of scalability, and by the difficulty of interpreting the related findings from a biological perspective. To tackle these limitations, we present Bayesian Genome-to-Phenome Sparse Regression (G2PSR), a novel multivariate regression method based on sparse SNP-gene constraints. The statistical framework of G2PSR is based on a Bayesian neural network, were constraints on SNPs-genes associations are integrated by incorporating knowledge linking variants to their respective genes, to then reconstruct the phenotypic data in the output layer.

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.

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.

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.

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.

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.

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.

Novel atlas of fiber directions built from ex-vivo diffusion tensor images of porcine hearts.

Cardiac MR image-based predictive models integrating statistical atlases of heart anatomy and fiber orientations can aid in better diagnosis of cardiovascular disease, a major cause of death worldwide. Such atlases have been built from diffusion tensor (DT) images and can be used in anisotropic models for personalized computational electro-mechanical simulations when the fiber directions from DTI are not available. In this paper, we propose a framework for building the first statistical fiber atlas from high-resolution ex-vivo DT images of porcine hearts.

Breaking the state of the heart: meshless model for cardiac mechanics.

Cardiac modeling has recently emerged as a promising tool to study pathophysiology mechanisms and to predict treatment outcomes for personalized clinical decision support. Nevertheless, achieving convergence under large deformation and defining a robust meshing for realistic heart geometries remain challenging, especially when maintaining the computational cost reasonable. Smoothed particle hydrodynamics (SPH) appears to be a promising alternative to the finite element method (FEM) since it removes the burden of mesh generation.