نبذة مختصرة : Metastases are a leading cause of death in the case of cancer. Indeed, these secondary tumors can develop in various organs, distant from the primary tumor, and occur at different times during the growth of the primary tumor. Many biomedical imaging techniques can be used to detect them. Among these, Magnetic Resonance Imaging (MRI) has the advantage of not using ionizing radiation and allows strong contrasts between different soft tissues. However, it remains necessary to develop new MRI techniques to better characterize tumors, obtain quantitative data and to drastically reduce exam times. Among the biophysical characteristics measurable by MRI, the T1 relaxation time seems to be a biomarker of the efficiency of an anti-cancer therapy. However, its measurement is generally too time consuming to be used in preclinical imaging on cohorts of animals, or for routine clinical use. This thesis therefore had the challenge of developing a reliable and rapid T1 quantification sequence. It has been applied to detect anc characterize metastases in mice with the prerequisites of generating images with high spatial resolution, being insensitive to respiratory movements and allowing reproducible T1 measurements. The Magnetization Prepared 2 Rapid Acquisition Gradient Echoes sequence (MP2RAGE) with Cartesian encoding was therefore chosen for its high T1 quantitation potential, its robustness to magnetic field heterogeneities, and its ability to quickly obtain 3D parametric maps. First, simulations were performed to evaluate the influence of sequence parameters. Then the sequence was modified to be compatible with an acceleration method called Compressed Sensing. This method was then used either to reduce the acquisition time of the T1 maps, or to improve the spatial resolution. This new MP2RAGE sequence was then applied at 7T to detect and characterize disseminated brain metastases in the mouse. The initial Cartesian encoding proved to be too sensitive to respiratory movements to detect liver metastases. It was therefore ...
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