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, Skødstrup, 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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