  In this paper, we present a deep learning algorithm to rapidly obtain high
quality CT reconstructions for AM parts. In particular, we propose to use CAD
models of the parts that are to be manufactured, introduce typical defects and
simulate XCT measurements. These simulated measurements were processed using
FBP (computationally simple but result in noisy images) and the MBIR technique.
We then train a 2.5D deep convolutional neural network [4], deemed 2.5D Deep
Learning MBIR (2.5D DL-MBIR), on these pairs of noisy and high-quality 3D
volumes to learn a fast, non-linear mapping function. The 2.5D DL-MBIR
reconstructs a 3D volume in a 2.5D scheme where each slice is reconstructed
from multiple inputs slices of the FBP input. Given this trained system, we can
take a small set of measurements on an actual part, process it using a
combination of FBP followed by 2.5D DL-MBIR. Both steps can be rapidly
performed using GPUs, resulting in a real-time algorithm that achieves the
high-quality of MBIR as fast as standard techniques. Intuitively, since CAD
models are typically available for parts to be manufactured, this provides a
strong constraint "prior" which can be leveraged to improve the reconstruction.
