AuthorsJ. L. Bruse, K. S. Mcleod, G. Biglino, H. N. Ntsinjana, C. Capelli, T. Hsia, M. Sermesant, X. Pennec, A. Taylor and S. Schievano
TitleA Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?
Afilliation, , Scientific Computing
Project(s)Center for Cardiological Innovation (SFI)
StatusPublished
Publication TypeProceedings, refereed
Year of Publication2015
Conference NameStatistical Atlases and Computational Modeling of the Heart (STACOM 2015)
EditionMICCAI Workshop
Date Published10/2015
PublisherLecture Notes in Computer Science, Springer. Verlag
KeywordsAortic Arch, Coarctation of the Aorta, Mathematical Currents, Non-parametric Statistical Shape Model, Partial Least Square Regression
Abstract

Coarctation of the Aorta (CoA) is a cardiac defect that re- quires 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 recom- mended 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 comput- ing 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 Mod- elling to be a promising diagnostic tool for improved screening of complex cardiac defects. 

Notes

Oral Presentation - Jan L. Bruse

Citation Key23826

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