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Abstract #1973

Deep learning-based reconstruction of highly-accelerated 3D MRI MPRAGE images

Sangtae Ahn1, Uri Wollner2, Graeme McKinnon3, Rafi Brada2, John Huston4, J. Kevin DeMarco5, Robert Y. Shih5,6, Joshua D. Trzasko4, Dan Rettmann7, Isabelle Heukensfeldt Jansen1, Christopher J. Hardy1, and Thomas K. F. Foo1
1GE Research, Niskayuna, NY, United States, 2GE Research, Herzliya, Israel, 3GE Healthcare, Waukesha, WI, United States, 4Mayo Clinic College of Medicine, Rochester, MN, United States, 5Walter Reed National Military Medical Center, Bethesda, MD, United States, 6Uniformed Services University of the Health Sciences, Bethesda, MD, United States, 7GE Healthcare, Rochester, MN, United States

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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