From Neural Networks to Neuroimaging: Evaluation of Deep Learning-Based MRI Reconstruction to Accelerate, Enhance and Improve Patient Outcomes

Motivation: The duration of MRI examinations can prove taxing for patients, leading to incomplete studies and compromised image quality due to motion. Critically ill patients requiring monitoring, those with MRI scanning time restricted implanted devices, and patients with altered mental status may be affected by lengthy scans. Goal(s): We aim to reduce scan times in neuroradiologic studies while maintaining or improving image quality. Approach: We applied accelerated acquisition and deep learning-based reconstruction to our current protocols. The images were assessed for signal-to-noise ratio and quality by two neuroradiologists. Results: Scan times were drastically reduced, some more than twofold, with simultaneous improvement of image quality. Impact: Deep learning-based reconstruction not only reduces MRI scanning time in common neuroradiologic examinations, but it also improves overall image quality. This empowers clinical sites to manage a higher workload while also diminishing potential for patient safety incidents.