  A novel deep learning-based magnetic resonance imaging reconstruction
pipeline was designed to address fundamental image quality limitations of
conventional reconstruction to provide high-resolution, low-noise MR images.
This pipeline's unique aims were to convert truncation artifact into improved
image sharpness while jointly denoising images to improve image quality. This
new approach, now commercially available at AIR Recon DL (GE Healthcare,
Waukesha, WI), includes a deep convolutional neural network (CNN) to aid in the
reconstruction of raw data, ultimately producing clean, sharp images. Here we
describe key features of this pipeline and its CNN, characterize its
performance in digital reference objects, phantoms, and in-vivo, and present
sample images and protocol optimization strategies that leverage image quality
improvement for reduced scan time. This new deep learning-based reconstruction
pipeline represents a powerful new tool to increase the diagnostic and
operational performance of an MRI scanner.
