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Abstract #4837

Gadgetron Inline AI: Effective Model inference on MR scanner

Hui Xue1, Rhodri Davies2, David Hansen3, Ethan Tseng4, Marianna Fontana5, James C. Moon2, and Peter Kellman1

1National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States, 2Barts Heart Centre, London, United Kingdom, 3Gradient Software, SkĂždstrup, Denmark, 4NIH, National Heart, Lung and Blood Institute, Bethesda, MD, United States, 5National Amyloidosis Centre, RoyalFree Hospital, London, United Kingdom

We extended Gadgetron, a widely used open-source framework, to support AI inference on clinical MR scanners. Specially designed software modules (InlineAI) was added to Gadgetron, allowing to load and apply AI neural network models on incoming MR data for compelte "in-line" fashion. That is, without any user interaction, results will be sent back to scanner and available immediately after data acquisition. Two AI based applications were developed as demenstration: Inline AI cine segmenation and perfusion flow mapping and analysis.

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