Towards HyperReduction of Cardiac Models Using Polyaffine Transformations This paper presents a method for framebased finite element models in order to develop fast personalised cardiac electromechanical models. Its originality comes from the choice of the deformation model: it relies on a reduced number of degrees of freedom represented by affine transformations located at arbitrary control nodes over a tetrahedral mesh. This is motivated by the fact that cardiac motion can be well represented by such polyaffine transformations. The shape functions use then a geodesic distance over arbitrary Voronoilike regions containing the control nodes. The high order integration of elastic energy density over the domain is performed at arbitrary integration points. This integration, which is associated to affine degrees of freedom, allows a lower computational cost while preserving a good accuracy for simple geometries. The method is validated on a cube under simple compression and preliminary results on simplified cardiac geometries are presented, reducing by a factor 100 the number of degrees of freedom.

Learning Interactions Between Cardiac Shape and Deformation: Application to Pulmonary Hypertension Cardiac shape and deformation are two relevant descriptors for the characterization of cardiovascular diseases. It is also known that strong interactions exist between them depending on the disease. In clinical routine, these high dimensional descriptors are reduced to scalar values (ventricular ejection fraction, volumes, global strains...), leading to a substantial loss of information. Methods exist to better integrate these highdimensional data by reducing the dimension and mixing heterogeneous descriptors. Nevertheless, they usually do not consider the interactions between the descriptors. In this paper, we propose to apply dimensionality reduction on high dimensional cardiac shape and deformation descriptors and take into account their interactions. We investigated two unsupervised linear approaches, an individual analysis of each feature (Principal Component Analysis), and a joint analysis of both features (Partial Least Squares) and related their output to the main characteristics of the studied pathology. We experimented both methods on right ventricular meshes from a population of 254 cases tracked along the cycle (154 with pulmonary hypertension, 100 controls). Despite similarities in the output space obtained by the two methods, substantial differences are observed in the reconstructed shape and deformation patterns along the principal modes of variation, in particular in regions of interest for the studied disease.