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International audience; Purpose: To evaluate the impact of a novel postprocessing denoising technique on accuracy and precision in myocardial T1 mapping.Materials and methods: This study introduces a fast and robust denoising method developed for magnetic resonance T1 mapping. The technique imposes edge-preserving regularity and exploits the co-occurence of spatial gradients in the acquired T1 -weighted images. The proposed approach was assessed in simulations, ex vivo data and in vivo imaging on a cohort of 16 healthy volunteers (12 males, average age 39 ± 8 years, 62 ± 9 bpm) both in pre- and postcontrast injection. The method was evaluated in myocardial T1 mapping at 3T with a saturation-recovery technique that is accurate but sensitive to noise. ROIs in the myocardium and left-ventricle blood pool were analyzed by an experienced reader. Mean T1 values and standard deviation were extracted and compared in all studies.Results: Simulations on synthetic phantom showed signal-to-noise ratio and sharpness improvement with the proposed method in comparison with conventional denoising. In vivo results demonstrated that our method preserves accuracy, as no difference in mean T1 values was observed in the myocardium (precontrast: 1433/1426 msec, 95%CI: [-40.7, 55.9], p = 0.75, postcontrast: 766/759 msec, 95%CI: [-60.7, 77.2], p = 0.8). Meanwhile, precision was improved with standard deviations of T1 values being significantly decreased (precontrast: 223/151 msec, 95%CI: [27.3, 116.5], p = 0.003, postcontrast: 176/135 msec, 95%CI: [5.5, 77.1], p = 0.03).Conclusion: The proposed denoising method preserves accuracy and improves precision in myocardial T1 mapping, with the potential to offer better map visualization and analysis.Level of evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1377-1388.

International audience; Exploiting redundancies between multiple images of an MRI examination can be formalized as the joint reconstruction of these images. The anatomy is preserved indeed so that specific constraints can be implemented (e.g. most of the features or spatial gradients should be in the same place in all these images) and only the contrast changes from one image to another need to be encoded. The application of this concept is particularly challenging in cardiovascular and body imaging due to the complex organ deformations, especially with the patient breathing. In this study a joint optimization framework is proposed for reconstructing multiple MR images together with a nonrigid motion model. The motion model takes into account both intra-image and inter-image motion and therefore can correct for most ghosting/blurring artifacts and misregistration between images. The framework was validated with free-breathing myocardial T 2 mapping experiments from nine heart transplant patients at 1.5 T. Results showed improved image quality and excellent image alignment with the multi-image reconstruction compared to the independent reconstruction of each image. Segment-wise myocardial T 2 values were in good agreement with the reference values obtained from multiple breath-holds (62.5 ± 11.1 ms against 62.2 ± 11.2 ms which was not significant with p=0.49).

Magnetic Resonance Imaging (MRI) has two main features. The first one, its ability to manipulate contrast, is a major advantage compared to the other imaging modalities. It allows to access complementary information for a better detectability and a diagnostic more accurate. This is especially useful for myocardium pathologies. The second feature of MRI is also one of its main drawbacks: the acquisition process is slow. Therefore, patient motion is a significant obstacle because it disturbs the acquisition process, which leads to artifacts in the reconstructed image. Cardiac and thoracic imaging are particularly sensitive to this motion issue. The aim of this thesis is to develop a new motion correction method that can be integrated in a multi-contrast workflow. In a first phase, we studied apart the motion correction problem. To do so, we focused more particularly on the GRICS method which was already developed in the IADI laboratory. This method allows the joint reconstruction of an image free from artifact and a non-rigid motion model that describes the displacements occurring during the acquisition. The first major contribution of this thesis is an improvement of the GRICS method consisting mainly in adapting it to the 3D imaging. This was achieved with a new adaptive regularization method that perfectly suits the inverse problem posed in GRICS. The second major contribution of this thesis consists in the simultaneous management of the motion correction on multiple acquisitions with different contrasts. To do so, the MRI examination is considered as a whole. Thus we make the most of information shared between the different contrasts. All these methods have been applied and validated by simulations, tests on phantom, on healthy volunteers and on patients as part of clinical studies. We aimed more particularly at cardiac MR. Finally the developed methods improve the acquisition and reconstruction workflow in the framework of a real clinical routine, L'Imagerie par Résonance Magnétique (IRM) présente deux caractéristiques principales. La première, sa capacité à manipuler le contraste, constitue son principal avantage par rapport aux autres modalités d'imagerie. Cela permet d'obtenir des informations complémentaires pour une meilleure détectabilité et une meilleure précision dans le diagnostic. Cela est particulièrement appréciable pour les pathologies du myocarde. La seconde caractéristique de l'IRM est également l'un de ces principaux inconvénients : le processus d'acquisition est relativement lent. De ce fait, les mouvements du patient constituent un obstacle important puisqu'ils perturbent ce processus d'acquisition, ce qui se traduit par des artéfacts dans l'image reconstruite. L'imagerie cardiaque et abdominale sont donc particulièrement sensibles à cette problématique du mouvement. L'objectif de cette thèse est donc de proposer une méthode de correction de mouvement intégrable dans un contexte multi-contraste. Nous avons étudié dans un premier temps la question de la correction de mouvement seule. Pour cela, nous nous sommes plus particulièrement intéressés à la méthode GRICS déjà développée au laboratoire IADI. Cette méthode permet la reconstruction conjointe d'une image sans artéfact et d'un modèle de mouvement non rigide permettant de corriger les déplacements qui surviennent pendant l'acquisition. Le premier apport majeur de cette thèse a consisté à améliorer la méthode GRICS, notamment pour l'adapter à l'imagerie volumique 3D. Il s'agit d'une nouvelle méthode de régularisation adaptative particulièrement adaptée au problème inverse posé dans GRICS. Le second apport majeur de cette thèse a consisté à gérer la correction de mouvement GRICS de manière conjointe sur des acquisitions présentant des contrastes différents. Il s'agit de concevoir l'examen IRM comme un tout et d'exploiter au mieux les informations partagées entre les différents contrastes. Toutes ces méthodes ont été appliquées et validées par des simulations, des tests sur fantôme, sur volontaires sains et sur des patients dans la cadre d'études cliniques. L'application cardiaque a été particulièrement visée. Les méthodes développées ont permis d'améliorer le processus d'acquisition et de reconstruction dans le contexte clinique réel