Abstract #3267

4D flow MRI hemodynamic quantification of pediatric patients with multi-site, multi-vender, and multi-channel machine learning segmentation

Takashi Fujiwara¹, Haben Berhane^2,3, Michael Baran Scott³, Zachary King², Michal Schafer⁴, Brian Fonseca⁴, Joshua Robinson³, Cynthia Rigsby^2,3, Lorna Browne⁴, Michael Markl³, and Alex Barker^1,5

¹Department of Radiology, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, Aurora, CO, United States, ²Lurie Children's Hospital of Chicago, Chicago, IL, United States, ³Northwestern University, Evanston, IL, United States, ⁴Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, Aurora, CO, United States, ⁵Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO, United States

A convolutional neural network (CNN) is presented to quantify 4D flow MRI-based hemodynamics using automated segmentation of the proximal vasculature. The intent is to reduce time and user variability for cumbersome 4D flow MRI analyses; however, the pediatric setting is challenging given the complex arterial geometry often seen in congenital heart diseases. Multi-site and -vender datasets were used to train a CNN for 3D segmentation. Flow quantification was conducted with the automated segmentations to test if datasets from multiple institutions and vendors improves flow quantification. We found the multi-site approach improved flow measurements in the setting of complex disease.

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