A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?
- Others:
- Cardiac Unit, Institute of Child Health (UCL) ; University College of London [London] (UCL)
- Great Ormond Street Hospital for Children [London] (GOSH)
- Analysis and Simulation of Biomedical Images (ASCLEPIOS) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
- Simula Research Laboratory [Lysaker] (SRL)
- COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)
- European Project: 600932,EC:FP7:ICT,FP7-ICT-2011-9,MD PAEDIGREE(2013)
Description
Coarctation of the Aorta (CoA) is a cardiac defect that requires surgical intervention aiming to restore an unobstructed aortic arch shape. Many patients suffer from complications post-repair, which are commonly associated with arch shape abnormalities. Determining the degree of shape abnormality could improve risk stratification in recommended screening procedures. Yet, traditional morphometry struggles to capture the highly complex arch geometries. Therefore, we use a non-parametric Statistical Shape Model based on mathematical currents to fully account for 3D global and regional shape features. By computing a template aorta of a population of healthy subjects and analysing its transformations towards CoA arch shape models using Partial Least Squares regression techniques, we derived a shape vector as a measure of subject-specific shape abnormality. Results were compared to a shape ranking by clinical experts. Our study suggests Statistical Shape Modelling to be a promising diagnostic tool for improved screening of complex cardiac defects.
Abstract
International audience
Additional details
- URL
- https://hal.inria.fr/hal-01205515
- URN
- urn:oai:HAL:hal-01205515v1
- Origin repository
- UNICA