Clinical Assessment of Deep Learning-based Super-Resolution for 3D Volumetric Brain MRI
Jeffrey D Rudie1, Tyler Gleason1, Matthew J Barkovich1
1Department of Radiology & Biomedical Imaging, University of California, San Francisco, 505 Parnassus Ave, L-352, San Francisco, CA 94143 (J.D.R., T.G., M.J.B., D.M.W., J.E.V.M.); Subtle Medical, Menlo Park, Calif (A.S., T.Z., L.W., E.G.); and Department of Radiology, Stanford University, Stanford, Calif (G.Z.).
Abstract:
Artificial intelligence (AI)-based image enhancement has the potential to reduce scan times while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study prospectively evaluated AI-based image enhancement in 32 consecutive patients undergoing clinical brain MRI. Standard-of-care (SOC) three-dimensional (3D) T1 precontrast, 3D T2 fluid-attenuated inversion recovery, and 3D T1 postcontrast sequences were performed along with 45% faster versions of these sequences using half the number of phase-encoding steps. Images from the faster sequences were processed by a Food and Drug Administration-cleared AI-based image enhancement software for resolution enhancement. Four board-certified neuroradiologists scored the SOC and AI-enhanced image series independently on a five-point Likert scale for image SNR, anatomic conspicuity, overall image quality, imaging artifacts, and diagnostic confidence. While interrater κ was low to fair, the AI-enhanced scans were noninferior for all metrics and actually demonstrated a qualitative SNR improvement. Quantitative analyses showed that the AI software restored the high spatial resolution of small structures, such as the septum pellucidum. In conclusion, AI-based software can achieve noninferior image quality for 3D brain MRI sequences with a 45% scan time reduction, potentially improving the patient experience and scanner efficiency without sacrificing diagnostic quality. Keywords: MR Imaging, CNS, Brain/Brain Stem, Reconstruction Algorithms © RSNA, 2022.
