Real-Time, Inline Quantitative MRI Enabled by Scanner-Integrated Machine Learning: A Proof of Principle With NODDI.
Authors
Affiliations (8)
Affiliations (8)
- Hawkes Institute and Department of Computer Science, UCL, London, UK.
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, UCL, London, UK.
- Research and Collaborations GBI, Siemens Healthcare Ltd, Camberley, UK.
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK.
- Neuroradiological Academic Unit, Dept of Translational Neuroscience and Stroke, UCL Queen Square Institute of Neurology, UCL, London, UK.
- Centre for Cardiovascular Imaging, Institute of Cardiovascular Science, UCL, London, UK.
- Department of Brain & Behavioural Sciences, University of Pavia, Pavia, Italy.
- Digital Neuroscience Centre, IRCCS Mondino Foundation, Pavia, Italy.
Abstract
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. The Siemens Image Calculation Environment (ICE) pipeline was customized to deploy trained NNs for advanced diffusion MRI parameter estimation with Open Neural Network Exchange (ONNX) Runtime. Two fully-connected NNs were trained offline with data synthesized with the neurite orientation dispersion and density imaging (NODDI) model, using either conventionally estimated (NN<sub>MLE</sub>) or ground truth (NN<sub>GT</sub>) parameters as training labels. The strategy was demonstrated online in two healthy volunteers (one rescanned) and evaluated offline with synthetic data, testing two diffusion protocols. NNs were successfully integrated and deployed natively in ICE, performing inline, whole-brain, in vivo NODDI parameter estimation in < 10 s. The proposed workflow was reproducible across protocols, volunteers and rescans. DICOM parametric maps were exported from the scanner for further analyses. Comparisons between NN<sub>MLE</sub> and NN<sub>GT</sub> suggested NN<sub>MLE</sub> parameter estimates to be more consistent with conventional fitting, a finding supported by offline evaluations. Real-time, inline parameter estimation with the proposed generalizable framework resolves a key practical barrier to the potential clinical uptake of advanced qMRI methods, enabling their efficient integration into clinical workflows. Next steps include incorporation of pre-processing methods and evaluation in pathology.