Tootfinder

Real-time, inline quantitative MRI enabled by scanner-integrated machine learning: a proof of principle with NODDI
Samuel Rot, Iulius Dragonu, Christina Triantafyllou, Matthew Grech-Sollars, Anastasia Papadaki, Laura Mancini, Stephen Wastling, Jennifer Steeden, John Thornton, Tarek Yousry, Claudia A. M. Gandini Wheeler-Kingshott, David L. Thomas, Daniel C. Alexander, Hui Zhang

Real-time, inline quantitative MRI enabled by scanner-integrated machine learning: a proof of principle with NODDI
Purpose: The clinical feasibility and translation of many advanced quantitative MRI (qMRI) techniques are inhibited by their restriction to 'research mode', due to resource-intensive, offline parameter estimation. This work aimed to achieve 'clinical mode' qMRI, by real-time, inline parameter estimation with a trained neural network (NN) fully integrated into a vendor's image reconstruction environment, therefore facilitating and encouraging clinical adoption of advanced qMRI techniques. Method…