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Abstract #2663

Probing the Feasibility and Performance of Super-Resolution Head and Neck MRA Using Deep Machine Learning

Ioannis Koktzoglou1,2, Rong Huang1, William J Ankenbrandt1,2, Matthew T Walker1,2, and Robert R Edelman1,3
1Radiology, NorthShore University HealthSystem, Evanston, IL, United States, 2University of Chicago Pritzker School of Medicine, Chicago, IL, United States, 3Northwestern University Feinberg School of Medicine, Chicago, IL, United States

Deep machine learning approaches offer the potential for improved super-resolution (SR) reconstruction which could be useful in many clinical applications. Patients with suspected stroke often undergo MRI, which often includes magnetic resonance angiography (MRA) of the head and neck arteries with scan times of ≈10 to 15 minutes using standard nonenhanced methods. With the aim of shortening scan times, we evaluated the feasibility and performance of four deep neural network (DNN)-based SR reconstructions for restoring the image quality and spatial resolution of thin slab stack-of-stars quiescent interval slice-selective (QISS) head and neck MRA with degraded slice resolution.

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