Optimization of Deep Learning-Accelerated 3D-T1-MPRAGE and Quantitative Assessment of Regional Brain Volumes Compared to Wave-CAIPI MPRAGE

Motivation: Deep-learning-accelerated MPRAGE holds potential to enhance image resolution, reduce acquisition times, and improve diagnostic precision. However, there is currently a lack of clinical validation regarding its performance in neuroimaging. Goal(s): To optimize and assess the performance of deep-learning-accelerated MPRAGE in comparison to Wave-CAIPI MPRAGE for non-contrast T1-weighted volumetric brain imaging. Approach: In this prospective clinical study, we systematically optimized and implemented a novel deep-learning-accelerated MPRAGE sequence and compared against Wave-CAIPI MPRAGE, a state-of-the-art acceleration method. Results: Deep-learning-accelerated MPRAGE enhances resolution and grey-white matter differentiation compared to Wave-CAIPI MPRAGE, with equivalent volumetric estimation in most brain regions. Impact: Deep-learning-accelerated MPRAGE yields sharper, higher-resolution images while preserving equivalent volumetric estimations. This technique holds significant potential for deploying deep learning across various medical imaging disciplines, potentially enabling faster and more precise disease characterization.