A computational approach on sensitivity of left ventricular wall strains to geometry

In this work we use a Finite Element model of the left ventricular\u3cbr/\u3e(LV) mechanics to assess the sensitivity of strains to geometry.\u3cbr/\u3eSix principal shape modes extracted from an atlas of LV geometries\u3cbr/\u3eusing principal component analysis, have been used to model the variability\u3cbr/\u3eof the geometry of a population of 1991 asymptomatic volunteers.\u3cbr/\u3eWe observed that shear strains are more sensitive than normal strains to\u3cbr/\u3egeometry. For all the strains, shape mode 1, related with variation in size\u3cbr/\u3ewithin the population, plays an major role, but none of the six principal\u3cbr/\u3emodes can be considered non influential

Validation of Equilibrated Warping-image registration with mechanical regularization-on 3D ultrasound images

International audienceImage registration plays a very important role in quantifying cardiac motion from medical images, which has significant implications in the diagnosis of cardiac diseases and the development of personalized cardiac computational models. Many approaches have been proposed to solve the image registration problem; however, due to the intrinsic ill-posedness of the image registration problem, all these registration techniques , regardless of their variabilities, require some sort of regularization. An efficient regularization approach was recently proposed based on the equilibrium gap principle, named equilibrated warping. Compared to previous work, it has been formulated at the continuous level within the finite strain hyperelasticity framework and solved using the finite element method. Regularizing the image registration problem using this principle is advantageous as it produces a realistic solution that is close to that of an hyperelastic body in equilibrium with arbitrary boundary tractions, but no body load. The equilibrated warping method has already been extensively validated on both tagged and untagged magnetic resonance images. In this paper, we provide full validation of the method on 3D ultrasound images, based on the 2011 MICCAI Motion Tracking Challenge data