  Purpose: To accelerate brain 3D MRI scans by using a deep learning method for
reconstructing images from highly-undersampled multi-coil k-space data
  Methods: DL-Speed, an unrolled optimization architecture with dense
skip-layer connections, was trained on 3D T1-weighted brain scan data to
reconstruct complex-valued images from highly-undersampled k-space data. The
trained model was evaluated on 3D MPRAGE brain scan data
retrospectively-undersampled with a 10-fold acceleration, compared to a
conventional parallel imaging method with a 2-fold acceleration. Scores of SNR,
artifacts, gray/white matter contrast, resolution/sharpness, deep gray-matter,
cerebellar vermis, anterior commissure, and overall quality, on a 5-point
Likert scale, were assessed by experienced radiologists. In addition, the
trained model was tested on retrospectively-undersampled 3D T1-weighted LAVA
(Liver Acquisition with Volume Acceleration) abdominal scan data, and
prospectively-undersampled 3D MPRAGE and LAVA scans in three healthy volunteers
and one, respectively.
  Results: The qualitative scores for DL-Speed with a 10-fold acceleration were
higher than or equal to those for the parallel imaging with 2-fold
acceleration. DL-Speed outperformed a compressed sensing method in quantitative
metrics on retrospectively-undersampled LAVA data. DL-Speed was demonstrated to
perform reasonably well on prospectively-undersampled scan data, realizing a
2-5 times reduction in scan time.
  Conclusion: DL-Speed was shown to accelerate 3D MPRAGE and LAVA with up to a
net 10-fold acceleration, achieving 2-5 times faster scans compared to
conventional parallel imaging and acceleration, while maintaining diagnostic
image quality and real-time reconstruction. The brain scan data-trained
DL-Speed also performed well when reconstructing abdominal LAVA scan data,
demonstrating versatility of the network.
