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Patient-specific cardiac modeling can help in understanding pathophysiology and therapy planning. However it requires to combine functional and anatomical data in order to build accurate models and to personalize the model geometry, kinematics, electrophysiology and mechanics. Personalizing the electromechanical coupling from medical images is a challenging task. We use the Bestel-Clément-Sorine (BCS) electromechanical model of the heart, which provides reasonable accuracy with a reasonable number of parameters (14 for each ventricle) compared to the available clinical data at the organ level. We propose a personalization strategy from cine MRI data in two steps. We first estimate global parameters with an automatic calibration algorithm based on the Unscented Transform which allows to initialize the parameters while matching the volume and pressure curves. In a second step we locally personalize the contractilities of all AHA (American Heart Association) zones of the left ventricle using the reduced order unscented Kalman filtering on Regional Volumes. This personalization strategy was validated synthetically and tested successfully on eight healthy and three pathological cases.

International audienceThis manuscript describes our recent developments towards better understanding of the mechanisms amenable to cardiac resynchronization therapy response. We report the results from a full multimodal dataset corresponding to eight patients from the euHeart project. The datasets include echocardiography, MRI and electrophysiological studies. We investigate two aspects. The first one focuses on pre-operative multimodal image data. From 2D echocardiography and 3D tagged MRI images, we compute atlas based dyssynchrony indices. We complement these indices with presence and extent of scar tissue and correlate them with CRT response. The second one focuses on computational models. We use pre-operative imaging to generate a patient-specific computational model. We show results of a fully automatic personalized electromechanical simulation. By case-per-case discussion of the results, we highlight the potential and key issues of this multimodal pipeline for the understanding of the mechanisms of CRT response and a better patient selection

Abstract-A statistical methodology is proposed to rank several estimation methods of a relevant clinical parameter when no gold standard is available. Based on a regression without truth method, the proposed approach was appliedtorankeightmethodswithout using any ap riori information regarding the reliability of each method and its degree of automation. It was only based on a prior concerning the statistical distribution of the parameter of interest in the database. The ranking of the methods relies on figures of merit derived from the regression and computed using a bootstrap process. The methodology was applied to the estimation of the left ventricular ejection fraction derived from cardiac magnetic resonance images segmented using eight approaches with different degrees of automation: three segmentations were entirely manually performed and the others were variously automated. The ranking of methods was consistent with the expected performance of the estimation methods: the most accurate estimates of the *J. Lebenberg is with the LIF, INSERM UMR_S 678, Université Pierre et Marie Curie, 75013 Paris, France, and also with the PRIAM, ESME-Sudria, 94200 Ivry-sur-Seine, France (e-mail: jessica.lebenberg@gmail.com).C. Constantinidès is with the LIF, INSERM UMR_S 678, Université Pierre et Marie Curie, 75013 Paris, France, and also with the PRIAM, ESME-Sudria, 94200 Ivry-sur-Seine, France.A .d eC e s a r e ,M .L e f o r t ,a n dF .F r o u i na r ew i t ht h eL I F ,I N S E R M UMR_S

An approach to the three-dimensional reconstruction of coronary arteries is presented. The principal objective is to show how modeling of a vascular network, together with algorithmic procedures, can lead to accurate 3-D structure and feature labeling. The labeling problem is stated directly within the 3-D reconstruction framework. The reconstruction ambiguities inherent to biplane techniques are solved by means of a knowledge base, modeling of the object, and heuristic rules. Feasibility in near-real situations has been demonstrated. The critical importance of the object 3-D reference to achieving the data and modeling matching is emphasized, and a way to deal with it is pointed out. The overall system implies an incremental development in methodologies and experiments. All of them have been elaborated and tested independently, and the most appropriate ones have been selected for integration into a modular system. All the stages of the process (calibration, segmentation, reconstruction, and display) are discussed, with the main focus on modeling. Examples of automatic reconstruction from a phantom are provided.

This paper aims to define and describe features of the motion of coronary arteries in two and three dimensions, presented as geometrical parameters that identify motion patterns. The main left coronary artery centerlines, obtained from digital subtraction angiography (DSA) image sequences, are first reconstructed. Thereafter, global and local motion features are evaluated along the sequence. The global attributes are centerline and point trajectory lengths, displacement amplitude, and virtual reference point, while local attributes are displacement direction, perpendicular/radial components, rotation direction, and curvature and torsion. These kinetic features allow us to obtain a detailed quantitative description of the displacements of arteries' centerlines, as well as associated epicardium deformations. Our modeling of local attributes as quasi-homogeneous on a segment analysis, enables us to propose a novel numeric to symbolic image transformation, which provides the required facts for knowledge-based motion interpretation. Experimental results using real data are consistent with cardiac dynamic behavior.

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