  Commercial iterative reconstruction techniques on modern CT scanners target
radiation dose reduction but there are lingering concerns over their impact on
image appearance and low contrast detectability. Recently, machine learning,
especially deep learning, has been actively investigated for CT. Here we design
a novel neural network architecture for low-dose CT (LDCT) and compare it with
commercial iterative reconstruction methods used for standard of care CT. While
popular neural networks are trained for end-to-end mapping, driven by big data,
our novel neural network is intended for end-to-process mapping so that
intermediate image targets are obtained with the associated search gradients
along which the final image targets are gradually reached. This learned dynamic
process allows to include radiologists in the training loop to optimize the
LDCT denoising workflow in a task-specific fashion with the denoising depth as
a key parameter. Our progressive denoising network was trained with the Mayo
LDCT Challenge Dataset, and tested on images of the chest and abdominal regions
scanned on the CT scanners made by three leading CT vendors. The best deep
learning based reconstructions are systematically compared to the best
iterative reconstructions in a double-blinded reader study. It is found that
our deep learning approach performs either comparably or favorably in terms of
noise suppression and structural fidelity, and runs orders of magnitude faster
than the commercial iterative CT reconstruction algorithms.
