Abstract
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.
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3D viewable models of the arches available under http://www.ucl.ac.uk/cardiac-engineering/research/library-of-3d-anatomies/congenital_defects/coarctations.
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Acknowledgments
This study is independent research by the National Institute for Health Research Biomedical Research Centre Funding Scheme. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. The authors gratefully acknowledge support from Fondation Leducq, FP7 Integrated Project MD-Paedigree, National Institute of Health Research, Commonwealth Scholarship Comission and Heart Research UK.
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Bruse, J.L. et al. (2016). A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2015. Lecture Notes in Computer Science(), vol 9534. Springer, Cham. https://doi.org/10.1007/978-3-319-28712-6_3
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DOI: https://doi.org/10.1007/978-3-319-28712-6_3
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