Quantification of cardiac motion and deformation from three-dimensional tagged MRI on data acquired on Philips scanners. (Quantification du mouvement et de la déformation cardiaques à partir d'IRM marquée tridimensionnelle sur des données acquises par des imageurs Philips).

Cardiovascular disease is one of the major causes of death worldwide. A number of heart diseases, such as hypertrophy, dilated cardiomyopathy and myocardial infarction, can be diagnosed through the analysis of cardiac images after quantifying shape and function. Recently, there is a surge in the development of fast 3D cardiac imaging techniques in both ultrasound (US) and magnetic resonance (MR) imaging, making it possible to quantify myocardial motion and strain fully in 3D. However, the application of these deformation quantification algorithms in clinical routine is somewhat held back by the lack of a solid validation. These quantification algorithms need to be thoroughly validated before being used in clinics. In this thesis, we mainly introduce a fast 3D tagged MR quantification algorithm, as well as a novel pipeline for generating synthetic cardiac US and MR image sequences for validation purposes. The main contributions are described below. First, we proposed a novel 3D extension of the well-known harmonic phase tracking method. The point-wise phase-based optical flow tracking was combined with an anatomical regularization model in order to estimate anatomically coherent myocardial motions. In particular, special efforts were made to ensure a reasonable radial strain estimation by enforcing myocardial incompressibility through the divergence theorem. We tuned the parameters on a synthetic tagged MR dataset. The proposed HarpAR algorithm was further evaluated on both healthy volunteers and patients having different levels of ischemia. On volunteer data, the tracking accuracy was found to be as accurate as the best candidates of a recent benchmark. On patient data, strain dispersion was shown to correlate with the extent of transmural fibrosis. Besides, the ischemic segments were distinguished from healthy ones from the strain curves. Second, we proposed a simulation pipeline for generating realistic synthetic cardiac US, cine and tagged MR sequences from the same virtual subject. Template sequences, a state-of-the-art electro-mechanical (E/M) model and physical simulators were combined in a unified framework. The E/M model was exploited for simulating groundtruth cardiac motion fields. The template sequences were registered to the simulations by a novel warping technique aimed at ensuring a synthetic motion consistent with the E/M model and a smooth transition between the myocardium and the background. Finally, backscattering amplitudes and effective proton densities were derived from the warped templates respectively for US and MR simulations to exploit the corresponding physical simulators for generating image data. In total, we simulated 18 virtual patients (3 healthy, 3 dyssynchrony and 12 ischemia), each with synthetic sequences of 3D cine MR, US and tagged MR. The synthetic images were assessed both qualitatively and quantitatively. They showed realistic image textures similar to real acquisitions. Besides, both the ejection fraction and regional strain values are in agreement with reference values published in the literature. Finally, we showed a preliminary benchmarking study using the synthetic database. The HarpAR algorithm initially developed for processing tagged MR was extended to a generic motion tracking algorithm named as gHarpAR. We performed a comparison between gHarpAR and another tracking algorithm SparseDemons using the virtual patients simulated in this thesis. The results showed that SparseDemons outperformed gHarpAR in processing cine MR and US images. Regarding tagged MR, both methods obtained similar accuracies on motion and two strain components (circumferential and longitudinal). However, gHarpAR quantified radial strains more accurately, thanks to the myocardial incompressibility constraint. We conclude that motion quantification solutions can be improved by designing them according to the image characteristics of the modality and that a solid evaluation framework can be a key asset in comparing different algorithmic options. iii Cette thèse est accessible à l'adresse : http://theses.insa-lyon.fr/publication/2017LYSEI058/these.pdf © [Y. Zhou], [2017], INSA Lyon, tous droits réservés