  Background and Objective: The strong demand for medical imaging applications
leads to the popularity of the CT reconstruction problem. Researchers proposed
multiple constraints to tackle none ideal factors in CT reconstruction such as
sparse-view, limited-angle, and low-dose conditions. Most of these constraints
such as total variation are local constraints focusing on the relationship
between a pixel and its neighbors. In this paper, we propose a new constraint
utilizing the global prior of CT images to greatly reduce the streak artifacts
and further improve the reconstruction accuracy. Methods: A CT image of the
human body contains a limited number of different types of tissues, so pixels
in CT images can be grouped into several groups according to their
corresponding types. In our work, we focus on the composition classification
for individual pixels and utilize it as a global prior, which differs from
priors utilized by most current constraints. We propose segmenting pixels based
on their gray levels during the reconstruction process, and forcing pixels in
the same group to have similar gray levels. Results: Our experiments on the
Shepp-Logan phantom and two real CT images from different benchmarks show that
the proposed constraint can help the conventional local constraints further
improve the reconstruction results under sparse-view, limited-angle, and
low-dose conditions. Conclusions: Different from most current constraints
focusing on the local prior, our proposed constraint only utilizes the global
prior of CT images. In that case, our proposed constraint can collaborate with
most local constraints and improve the reconstruction quality significantly.
Furthermore, the proposed constraint also has the potential for further
improvement, as the composition classification can be done with some more
delicate methods, such as neural network related semantic segmentation
algorithms.
