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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