Feasibility of a longitudinal statistical atlas model to study aortic growth in congenital heart disease

@Article {
author = {Jan Lukas Bruse},
title = {Feasibility of a longitudinal statistical atlas model to study aortic growth in congenital heart disease},
journal = {Computers in Biology and Medicine},
pages = {105326},
volume = {144},
keywds = {
Longitudinal atlas, Computational growth model, Statistical shape analysis, Congenital heart disease, Bicuspid aortic valve, Magnetic resonance imaging, Cardiovascular, 3D modelingCongenital heart disease, Bicuspid aortic valve, Magnetic resonanc
}
abstract = {

Studying anatomical shape progression over time is of utmost importance to refine our understanding of clinically relevant processes. These include vascular remodeling, such as aortic dilation, which is particularly important in some congenital heart defects (CHD). A novel methodological framework for three-dimensional shape analysis has been applied for the first time in a CHD scenario, i.e., bicuspid aortic valve (BAV) disease, the most common CHD. Three-dimensional aortic shapes (n = 94) reconstructed from cardiovascular magnetic resonance imaging (MRI) data as surface meshes represented the input for a longitudinal atlas model, using multiple scans over time (n = 2–4 per patient). This model relies on diffeomorphism transformations in the absence of point-to-point correspondence, and on the right combination of initialization, estimation and registration parameters. We computed the shape trajectory of an average disease progression in our cohort, as well as time-dependent parameters, geometric variations and the average shape of the population. Results cover a spatiotemporal spectrum of visual and numerical information that can be further used to run clinical associations. This proof-of-concept study demonstrates the feasibility of applying advanced statistical shape models to track disease progression and stratify patients with CHD

}
doi = {10.1016/j.compbiomed.2022.105326},
date = {2022-02-28},
}