Abstract #3651

Evaluation of PC-MRI Flow in Adults Using a Deep Learning-Based Reconstruction Trained on Pediatric Data

Matthew J. Middione¹, Julio A. Oscanoa^1,2, Xianglun Mao³, Christina R. Ruiz⁴, Michael Salerno^1,4,5, Shreyas S. Vasanawala¹, and Daniel B. Ennis^1,5

¹Department of Radiology, Stanford University, Stanford, CA, United States, ²Department of Bioengineering, Stanford University, Stanford, CA, United States, ³GE HealthCare, Menlo Park, CA, United States, ⁴Department of Medicine, Stanford University, Stanford, CA, United States, ⁵Cardiovascular Institute, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Flow, Machine Learning/Artificial Intelligence, Phase ContrastWe have previously trained a deep learning-based (DL) reconstruction for 2D PC-MRI using fully-sampled (n=194) raw k-space pediatric datasets. This DL-based reconstruction provided up to 9x undersampling with ≤5% error in accuracy and precision of peak velocity and total flow. Herein, we analyze this pediatric trained DL-based reconstruction in adult volunteers (n=3) and adult patients (n=8) and show that our DL-based reconstruction provides ~5% error in accuracy and precision of peak velocity and total flow for up to 7x undersampling.

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