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Abstract #0340

Real-time quantitative MRI enabled by scanner integrated machine learning: a proof of principle with NODDI

Samuel Rot1,2, Iulius Dragonu3, David Thomas4, Daniel C. Alexander1, and Hui Zhang1
1Hawkes Institute and Department of Computer Science, UCL, London, United Kingdom, UCL, London, United Kingdom, 2NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, UCL, London, United Kingdom, 3Research and Collaborations GBI, Siemens Healthcare Ltd, Camberley, United Kingdom, 4Neuroradiological Academic Unit, Dept of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, UCL, London, UK, UCL, London, United Kingdom

Synopsis

Keywords: Machine Learning/Artificial Intelligence, AI/ML Software

Motivation: Resource intensive offline image post-processing and parameter estimation inhibit the clinical integration and feasibility of quantitative MRI (qMRI).

Goal(s): To achieve real-time, online qMRI parameter estimation with a trained neural network (NN) integrated into a vendor’s image reconstruction environment, facilitating and encouraging clinical adoption of qMRI.

Approach: A NN for estimating parameters of the NODDI diffusion MRI model was trained offline using synthetic data. Its trained state was exported into ONNX format (a cross-language ML framework) and integrated into a custom Siemens Image Calculation Environment (ICE) module.

Results: The integrated NN performs accurate, online, real-time NODDI parameter estimation of in vivo data.

Impact: Real-time, online qMRI parameter estimation with the presented strategy resolves a key practical barrier to clinical uptake of qMRI methods and enables their efficient integration into clinical workflows.

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Keywords