Advanced Computational Methods for Multidimensional Data: Application to Functional MRI

PECRE Award Holder: Dr Abderrahim Halimi, Heriot-Watt University

Exchange Host: The French Alternative Energies and Atomic Energy Commission (CEA), France

Advanced imaging systems acquire large volumes of multidimensional data that might be incomplete and/or corrupted due to the modality of acquisition, the environment of observation, or the instrument configuration/limitations. To address these difficulties, it is imperative to improve such systems by increasing the robustness of the measure and optimizing the data processing strategies. My main objective in this project is to develop new computational imaging methods to solve important tasks (e.g., help diagnosis, parameter estimation for decision making), related to medical imagery systems to restore the acquired data under real-world conditions. More precisely, the objectives are to (i) improve data
robustness to extreme environments and dynamic scenes by considering sophisticated physical and statistical models, and (ii) optimize the processing of the acquired data by developing advanced and computationally efficient algorithms. Ultimately, the project will lead to advanced statistical methods that will directly impact the processing of medical data (e.g., MRI and fMRI) and will be adapted to other high dimensional data acquired using similar compressive sensing approaches (e.g., single photon-based microscopy imaging or 3D imaging). This project is very timely and will allow me to reinforce the interaction between medical imagery experts from France (Neurospin-INRIA-Parietal) and Heriot-Watt
University. In long term, the project will also involve UK universities (collaborations through ESRC, and UK-Quantum Hub) and industrial partners (e.g. Renishaw).