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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