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Abstract #3270

Automatic Vascular Function Estimation using Deep Learning for Dynamic Contrast-enhanced Magnetic Resonance Imaging

Wallace Souza Loos1,2, Roberto Souza2,3, Linda Andersen1,2, R. Marc Lebel2,4, and Richard Frayne1,2
1Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada, 2Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada, 3Electrical and Computer Engineering, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada, 4General Electric Healthcare, Calgary, AB, Canada

Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging is an important clinical tool for investigating the cerebrovascular microcirculatory systems including the blood-brain barrier through estimation of perfusion and permeability maps. However, a vascular function is needed to generate these maps. The estimation is usually performed manually, potentially leading to error, and is also time-consuming. In this work, we designed a deep learning model that leverages the temporal and spatial information from a time series of DCE-MR images to estimate the vascular function automatically. Our model was able to generalize well for unseen data and achieved good overall performance.

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