Medical imaging plays a continuously increasing role in diagnosis or therapeutic follow up of multiple pathologies. Magnetic Resonance Imaging (MRI) in particular is a multi-purpose modality combining unique features. For brain characterisation, it stands as the reference method. With longer average lifespans, cerebral diseases related to aging increasingly become a public health problem. Cancer, dementia, multiple sclerosis, Alzheimer's and Parkinson's diseases, and acute or degenerative neuropathologies are defined by national authorities as major topics for medical research deserving strong support. Their detailed characterization heavily relies on MRI. However, recent advanced neuro-imaging techniques allowing functional or metabolic assessment of tissue often require long scan times. Cost and duration of the examination are increased, as well as the risk of data corruption from subject motion. This occurs particularly frequently with children or the elderly. Fast spatial encoding of MR signal using non-cartesian sampling trajectories and parallel sen- sitivity encoding approaches can improve advanced applications like functional MRI (fMRI), arterial spin labelling (ASL) perfusion, spectroscopy and white matter fibre tracking from diffusion tensor images (DTI). Quantification of small signal variations would be further enhanced by dynamic adaptation of the acquisition parameters to head motion of the subject. Our project aims at a proof of concept in the field of Technology for Health, with software Engineering aspects. It has the ambition to combine fast encoding and real-time adaptation approaches, in order to accelerate transfer to the clinic of research developments and to open new neuro-imaging applications on commercially available MRI scanners. We have built, on research equipment, expert know-how in implementing techniques for fast multi-dimensional spatial encoding associated with irregular sampling of image Fourier space. Original variants of an appropriate reconstruction algorithm and improved calibration of acquisition techniques based on this approach have been locally conceived and implemented. We propose to adapt and extend these developments to modern clinical MRI scanners, state-of-the-art notably by their parallel spatial sensitivity encoding possibilities and their potential for real time adaptation of acquisition parameters. W e will evaluate the benefit to neuro-imaging applications in research and clinical contexts. Algorithms, codes and software developed within the project will be license-protected, implying agreement prior to commercial or industrial use, and engagement to citation prior to academic exploitation. Care will be taken to protect further inventions to appear during the project. Another very important valorization target is transfer to the clinic of the advanced neuro-imaging protocols developed.