  Industrial CT is useful for defect detection, dimensional inspection and
geometric analysis. While it does not meet the needs of industrial mass
production, because of its time-consuming imaging procedure. This article
proposes a novel stationary real-time CT system with multiple X-ray sources and
detectors, which is able to refresh the CT reconstructed slices to the detector
frame frequency. This kind of structure avoids the movement of the X-ray
sources and detectors. Projections from different angles can be acquired with
the objects' translation, which makes it easier to be integrated into pipeline.
All the detectors are arranged along the conveyor, and observe the objects in
different angle of view. With the translation of objects, their X-ray
projections are obtained for CT reconstruction. To decrease the mechanical size
and reduce the number of X-ray sources and detectors, the FBP reconstruction
algorithm was combined with deep-learning image enhancement. Medical CT images
were applied to train the deep-learning network for its quantity advantage in
comparison with industrial ones. It is the first time to adopt this
source-detector layout strategy. Data augmentation and regularization were used
to elevate the generalization of the network. Time consumption of the CT
imaging process was also calculated to prove its high efficiency. It is an
innovative design for the 4th industrial revolution, providing an intelligent
quality inspection solution for digital production.
