Manifold parametrization of the left ventricle for a statistical modelling of its complete anatomy

D. Gil; J. Garcia-Barnes; A. Hernández-Sabate; E. Marti

doi:10.1117/12.844480

12 March 2010 Manifold parametrization of the left ventricle for a statistical modelling of its complete anatomy

D. Gil, J. Garcia-Barnes, A. Hernández-Sabate, E. Marti

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 762304 (2010) https://doi.org/10.1117/12.844480
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

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.

Citation Download Citation

D. Gil, J. Garcia-Barnes, A. Hernández-Sabate, and E. Marti "Manifold parametrization of the left ventricle for a statistical modelling of its complete anatomy", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762304 (12 March 2010); https://doi.org/10.1117/12.844480

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