Resumen de Computational anatomy as a driver of understanding structural and functional cardiac remodelling
Cardiac structural and functional remodelling, induced by altered or adverse working conditions, has been extensively reported in the literature. The quantification and interpretation of such remodelling is still an ongoing research topic, and the mechanisms and lasting effects are not completely understood. A difficulty in understanding remodelling is that, even if at cellular level each cell acts independently, it is the organ-level aggregation that will ultimately determine the cardiac efficiency. Given that remodelling is driven by regional stimuli, it will not be homogeneous but result in complex segmental patterns. The assessment through traditional methods is challenging due to the focus on global quantification of the current clinical measurements, as well as the large heterogeneity between the responses of individuals.
We present a statistical shape analysis (SSA) framework to identify the expected appearance of regional shape remodelling. We use a pathological and control population to build a model that identifies populational differences in shape. This model consists of a dimensionality reduction step (PCA and PLS) and a classification step (logistic regression). Given the large natural shape variability present in the ventricles, that can create a confounding effect if there is an imbalance of the demographics, we applied and tested methods to account for that variability: adding the confounders as covariates in the classification model (adjustment) and building a model that predicts shape from the confounders, and analysing the regression residuals (confounder deflation). We show that these methods are able to correct for demographic imbalances.
The previous methodology was applied to two distinct cardiac magnetic resonance imaging (MRI) datasets: one with triathletes and another one with small-for-gestational age (SGA) individuals, which were compared to a control population to obtain the remodelling. SGA is a syndrome occurring at foetal stage where the individual has an impaired growth during foetal development. It is hypothesized that it translates to a higher cardiovascular risk during seniority. Using our framework, we were able to identify that SGA presented a more curved base in the right ventricle (RV) curved than controls, especially in smokers and overweight SGA individuals. Athletes were also compared to controls in order to obtain characterize endurance-activities-related remodelling. We identified a pattern that was comprised of the already known remodelling: an increase of the left ventricular (LV) volume size and mass, but also an increase of RV volume localized in the outflow. The quantification of the athletic remodelling was associated to a better cardiovascular response during a maximal stress test.
Finally, we explore alternatives to analyse regional shape of the RV that did not use the point-to-point registration when the imaging modality is noisy and with low contrast, such as 3D echocardiography. We implemented a mesh independent volumetric parcellation of the RV in three parts: inlet, outflow and apex. The parcellation was defined on the surface using the geodesic distances to the apex, tricuspid and pulmonary valve, and was propagated to the cavity using Laplace equation. We tested the reproducibility of the parcellation and found an acceptable mean error (~8%) in the intraobserver test, and a higher (>14%) for the interobserver. We validated the method in a synthetic-remodelling generated dataset and found that our method was accurate for capturing circumferential dilation but was not suited for longitudinal elongations.
