Joint Myocardial Motion and Contraction Phase Estimation from Cine MRI using Variational Data Assimilation Viateur Tuyisenge 1 , Laurent Sarry 1 , Thomas Corpetti 3 , Elisabeth Coupez 2 , Lemlih Ouchchane 1 and Lucie Cassagnes 1,2 1 Clermont Universit´ e, Universit´ e d’ Auvergne, ISIT UMR 6284 UdA-CNRS Clermont-Ferrand, France 2 Pˆ ole de Radiologie et d’ Imagerie M´ edicale, CHU Gabriel Montpied, Clermont-Ferrand, France 3 COSTEL-LETG UMR 6554 CNRS-Universit´ e de Rennes 2, Rennes, France Abstract. We present a cardiac motion estimation method with vari- ational data assimilation that combines image observations and a dy- namic evolution model. The novelty of the model is that it embeds new parameters modeling heart contraction and relaxation. It was applied to a synthetic dataset with known ground truth motion and to 10 cine- MRI sequences of patients with normal or dyskinetic myocardial zones. It was compared to the inTag tagging tracking software for computing the radial motion component, and to the diagnosis for dyskinesia. We found that the new dynamic model performed better than the standard transport model, and the contraction parameters are promising features for diagnosing dyskinesia. 1 Introduction Non-invasive image-based analysis and quantification of cardiac motion provide important information on how pathology affects local and global deformation of the myocardium and its responses to a given therapy. The estimation of myocar- dial deformations helps to study and detect regions with an abnormal contraction in order to provide treatment for recovery. It includes a comprehensive study of the structural or architectural heart abnormalities, which provides an essential prognostic approach for therapeutic decisions, such as the implantation of defib- rillators for infarction resynchronization, the adaptation of doses or treatments such as beta-blockers converting enzyme inhibitors. Over the past few years, considerable efforts have been made to develop meth- ods to track the myocardium in different available imaging modalities, such as ultrasound (US), magnetic resonance imaging (cine MRI or tagged MRI) or SPECT. However, accurate cardiac motion estimation is still a challenging and open problem due to low spatial and temporal resolutions and the complexity of the cardiac biomechanics [1]. Numerous categories of methods aim to ob- tain cardiac contraction parameters and deformation fields from images and the STACOM2014, 005, v2 (final): ’Joint Myocardia...’ 1