Feasibility of a longitudinal statistical atlas model to study aortic growth in congenital heart disease. (3rd October 2022)

• Record Type: Journal Article
• Authors: Sophocleous, F; Bone, A; Shearn, A I U; Nieves Velasco Forte, M; Bruse, J L; Caputo, M; Biglino, G
• Is Part Of: European heart journal. Volume 43(2022)Supplement 2
• Publication Date: 2022-10-03
• Subjects: Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehac544.260
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: Studying anatomical shape progression over time is of utmost importance to refine our understanding of clinically relevant processes. These include vascular remodelling, such as aortic dilation, which is particularly important in some congenital heart defects (CHD). Purpose: A novel methodological framework for analysing three-dimensional (3D) shape changes over time ("growth") has been applied for the first time in a CHD scenario, i.e., bicuspid aortic valve (BAV) disease, the most common CHD. Methods: Three-dimensional aortic shapes (n=94) reconstructed as surface meshes from cardiovascular magnetic resonance imaging (MRI) data represented the input for a longitudinal shape atlas model, using multiple scans over time (n=2–4 scans per patient). This model relies on diffeomorphic transformations in the absence of point-to-point correspondence, and on the correct combination of initialization, estimation, and registration parameters. Results: We computed the 3D shape trajectory of an average disease progression over time in our cohort (Picture 1, grey to blue shapes), as well as time-dependent parameters, geometric variations and the average shape of the population (Picture 1, red shape). Results cover a spatiotemporal spectrum of visual and numerical information that can be further used to investigate clinical associations and stratify patients, as such capturing aortic remodelling in the presence of BAV aortopathy. Conclusion: This proof-of-concept study demonstrates the feasibility of applying advanced statistical shape models to track disease progression and stratify patients with CHD. Funding Acknowledgement: Type of funding sources: Foundation. Main funding source(s): British Heart Foundation and NIHR BRC