PET/MRI offers advantages in comparison with the clinically accepted PET/CT: higher soft tissue contrast, minimum radiation exposure to the patient and simultaneous data acquisition. The goal of dynamic studies is to provide information about physiological parameters. The evaluation of different parametric brain images brings new understanding of the brain and diseases. This is a new field of medical imaging called Parametric Imaging. Clinically, parametric PET and MRI images are interesting for a range of diseases such as early detection of Alzheimer’s Disease and tumour evaluation. For studying the brain and diseases parametrically, dynamic information becomes essential, because most changes of state in the brain occur at a very fast rate (milliseconds). Therefore, brain PET studies use dynamic scans, in which the activity distribution is measured over time. For obtaining the physiological parameters, it is necessary to apply kinetic models. Kinetic models require two time courses: Input Function (IF), which is the time course of the activity delivered to the tissue; and Time Activity Curves (TAC) of each voxel, which is the variation in activity over time. However, estimation of kinetic parameters may be rather difficult due to the uncertainties in TAC and in the IF. In this proposal, the main objective is to improve dynamic PET information. This objective will be achieved by improving the estimation of the IF and TAC. A novel method for estimating the IF is proposed here. The IF is estimated with an Image-Derived Input Function procedure using MRI information to correct the partial volume effect of the images. This MRI-based partial volume correction is based on priors. The TAC are going to be improved by new regularization methods, which are also based on the application of priors. The outcomes intended for this project are: evaluation of the developed methods in terms of kinetic parameters and Parametric PET/MR data are analyzed in the clinical context.