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Proceedings Paper

Manifold parametrization of the left ventricle for a statistical modelling of its complete anatomy
Author(s): D. Gil; J. Garcia-Barnes; A. Hernández-Sabate; E. Marti
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Paper Abstract

Distortion of Left Ventricle (LV) external anatomy is related to some dysfunctions, such as hypertrophy. The architecture of myocardial fibers determines LV electromechanical activation patterns as well as mechanics. Thus, their joined modelling would allow the design of specific interventions (such as peacemaker implantation and LV remodelling) and therapies (such as resynchronization). On one hand, accurate modelling of external anatomy requires either a dense sampling or a continuous infinite dimensional approach, which requires non-Euclidean statistics. On the other hand, computation of fiber models requires statistics on Riemannian spaces. Most approaches compute separate statistical models for external anatomy and fibers architecture. In this work we propose a general mathematical framework based on differential geometry concepts for computing a statistical model including, both, external and fiber anatomy. Our framework provides a continuous approach to external anatomy supporting standard statistics. We also provide a straightforward formula for the computation of the Riemannian fiber statistics. We have applied our methodology to the computation of complete anatomical atlas of canine hearts from diffusion tensor studies. The orientation of fibers over the average external geometry agrees with the segmental description of orientations reported in the literature.

Paper Details

Date Published: 12 March 2010
PDF: 9 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762304 (12 March 2010); doi: 10.1117/12.844480
Show Author Affiliations
D. Gil, Univ. Autònoma de Barcelona (Spain)
J. Garcia-Barnes, Univ. Autònoma de Barcelona (Spain)
A. Hernández-Sabate, Univ. Autònoma de Barcelona (Spain)
E. Marti, Univ. Autònoma de Barcelona (Spain)


Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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