Bruse, JL;
McLeod, K;
Biglino, G;
Ntsinjana, HN;
Capelli, C;
Hsia, TY;
Sermesant, M;
... Schievano, S; + view all
(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, C and Mansi, T and Pop, M and Rhode, K and Sermesant, M and Young, A, (eds.)
Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges.
(pp. pp. 21-29).
Springer: Cham, Switzerland.
Preview |
Text
Bruse_STACOM_UNFORMATTED_2015.pdf Download (671kB) | Preview |
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.
| Type: | Proceedings paper |
|---|---|
| Title: | A non-parametric statistical shape model for assessment of the surgically repaired aortic arch in coarctation of the aorta: How normal is abnormal? |
| Event: | Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges 2016 |
| ISBN-13: | 9783319287119 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-319-28712-6_3 |
| Publisher version: | https://doi.org/10.1007/978-3-319-28712-6_3 |
| Language: | English |
| Additional information: | Copyright © Springer International Publishing Switzerland 2016. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-28712-6_3 |
| Keywords: | Non-parametric statistical shape model, Mathematical currents, Partial least square regression, Coarctation of the aorta, Aortic arch |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Childrens Cardiovascular Disease |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1501523 |
Archive Staff Only
 |
View Item |
