Helene Langet

Gabriel Bernardino, Amir Hodzic, Helene Langet et al.

3D echocardiography is an increasingly popular tool for assessing cardiac remodelling in the right ventricle (RV). It allows quantification of the cardiac chambers without any geometric assumptions, which is the main weakness of 2D echocardiography. However, regional quantification of geometry and function is limited by the lower spatial and temporal resolution and the scarcity of identifiable anatomical landmarks. We developed a technique for regionally assessing the 3 relevant RV regions: apical, inlet and outflow. The method's inputs are end-diastolic (ED) and end-systolic (ES) segmented 3D surface models. The method first defines a partition of the ED endocardium using the geodesic distances from each surface point to apex, tricuspid valve and pulmonary valve: the landmarks that define the 3 regions. The ED surface mesh is then tetrahedralised, and the endocardial-defined partition is interpolated in the blood cavity via the Laplace equation. For obtaining an ES partition, the endocardial partition is transported from ED to ES using a commercial image-based tracking, and then interpolated towards the endocardium, similarly to ED, for computing volumes and ejection fraction (EF). We present a full assessment of the method's validity and reproducibility. First, we assess reproducibility under segmentation variability, obtaining intra- and inter- observer errors (4-10% and 10-23% resp.). Finally, we use a synthetic remodelling dataset to identify the situations in which our method is able to correctly determine the region that has remodelled. This dataset is generated by a novel mesh reconstruction method that deforms a reference mesh, locally imposing a given strain, expressed in anatomical coordinates. We show that the parcellation method is adequate for capturing local circumferential and global circumferential and longitudinal RV remodelling.

Esther Puyol-Anton, Bram Ruijsink, Helene Langet et al.

The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function. Cardiac motion atlases provide a space of reference in which the motion fields of a cohort of subjects can be directly compared. In this work, a cardiac motion atlas is built from cine MR data from the UK Biobank (~ 6000 subjects). Two automated quality control strategies are proposed to reject subjects with insufficient image quality. Based on the atlas, three dimensionality reduction algorithms are evaluated to learn data-driven cardiac motion descriptors, and statistical methods used to study the association between these descriptors and non-imaging data. Results show a positive correlation between the atlas motion descriptors and body fat percentage, basal metabolic rate, hypertension, smoking status and alcohol intake frequency. The proposed method outperforms the ability to identify changes in cardiac function due to these known cardiovascular risk factors compared to ejection fraction, the most commonly used descriptor of cardiac function. In conclusion, this work represents a framework for further investigation of the factors influencing cardiac health.