Abstract
We introduce a tool to reconstruct a geometrical surface mesh from sparse, heterogeneous, non coincidental contours and show its application to cardiac data. In recent years much research has looked at creating personalised 3D anatomical models of the heart. These models usually incorporate a geometrical reconstruction of the anatomy in order to understand better cardiovascular functions as well as predict different processes after a clinical event. The ability to accurately reconstruct heart anatomy from MRI in three dimensions commonly comes with fundamental challenges, notably the trade off between data fitting and regularization. Most current techniques requires data to be either parallel, or coincident, and bias the final result due to prior shape models or smoothing terms. Our approach uses a composition of smooth approximations towards the maximization of the data fitting. Assessment of our method was performed on synthetic data obtained from a mean cardiac shape model as well as on clinical data belonging to one normal subject and one affected by hypertrophic cardiomyopathy. Our method is both used on epicardial and endocardial left ventricle surfaces, but as well as on the right ventricle.
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Acknowledgments
BV acknowledges the support of the RCUK Digital Economy Programme grant number EP/G036861/1 (Oxford Centre for Doctoral Training in Healthcare Innovation). VC was supported by ERACoSysMed through a grant to the project SysAFib - Systems medicine for diagnosis and stratification of atrial fibrillation. RA is supported by a British Heart Foundation Clinical Research Training Fellowship. VG is supported by a BBSRC grant (BB/I012117/1), an EPSRC grant (EP/J013250/1) and by BHF New Horizon Grant NH/13/30238. EZ acknowledges the Marie Sklodowska-Curie Individual Fellowship from the H2020 EU Framework Programme for Research and Innovation [Proposal No: 655020-DTI4micro-MSCA-IF-EF-ST].
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Villard, B., Carapella, V., Ariga, R., Grau, V., Zacur, E. (2017). Cardiac Mesh Reconstruction from Sparse, Heterogeneous Contours. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_15
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DOI: https://doi.org/10.1007/978-3-319-60964-5_15
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