Volumetric parcellation of the cardiac right ventricle for regional geometric and functional assessment

Bernardino Perez, Gabriel; Hodzic, Amir; Langet, Hélène; Legallois, Damien; Craene, Mathieu de; González Ballester, Miguel Ángel, 1973-; Saloux, Eric; Bijnens, Bart

doi:http://dx.doi.org/10.1016/j.media.2021.102044

Volumetric parcellation of the cardiac right ventricle for regional geometric and functional assessment

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Bernardino G, Hodzic A, Langet H, Legallois D, De Craene M, González Ballester MA, Saloux E, Bijnens B. Volumetric parcellation of the cardiac right ventricle for regional geometric and functional assessment. Med Image Anal. 2021;71:102044. DOI: 10.1016/j.media.2021.102044

Abstract

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, especially within the ventricular cavity. We developed a technique for regionally assessing the volume of 3 relevant RV volumetric regions: apical, inlet and outflow. The proposed parcellation method is based on the geodesic distances to anatomical landmarks that are easily identifiable in the images: the apex and the tricuspid and pulmonary valves, each associated to a region. Based on these distances, we define a partition in the endocardium at end-diastole (ED). This partition is then interpolated to the blood cavity using the Laplace equation, which allows to compute regional volumes. For obtaining an end-systole (ES) partition, the endocardial partition is transported from ED to ES using a commercial image-based tracking software, and then the interpolation process is repeated. We assessed the intra- and inter-observer reproducibility using a 10-subjects dataset containing repeated quantifications of the same images, obtaining intra- and inter- observer errors (7 - 12 % and 10 - 23 % respectively). Finally, we propose a novel synthetic mesh generation algorithm that deforms a template mesh imposing a user-defined strain to a template mesh. We used this method to create a new dataset for involving distinct types of remodelling that were used to assess the sensitivity of the parcellation method to identify volume changes affecting different parts. We show that the parcellation method is adequate for capturing local circumferential and global circumferential and longitudinal RV remodeling, which are the most clinically relevant cases.