Three-dimensional (3D) MRI can achieve higher spatial resolution and signal-to-noise ratio than 2D MRI at the expense of long scan times. Recently, deep-learning (DL) techniques have been applied to reconstruction from highly undersampled data, resulting in significant scan accelerations. To assess clinical acceptability, we evaluated DL-based reconstruction on 3D MPRAGE data, using scores from image evaluation by neuroradiologists. Our DCI-Net method with reduction factor R=10 received scores higher than or equal to those of conventional parallel imaging with R=2.1. This implies the DL method can accelerate scans by an additional factor of 5 while maintaining comparable diagnostic image quality.
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