A Global Constraint to Improve CT Reconstruction Under Non-Ideal Conditions

TL;DR

This paper introduces a novel global prior constraint for CT image reconstruction that groups pixels by tissue type, reducing artifacts and enhancing accuracy under challenging conditions like sparse-view and low-dose scans.

Contribution

The work proposes a new global prior based on tissue classification, which complements existing local constraints to improve CT reconstruction quality.

Findings

The global prior constraint reduces streak artifacts significantly.

Combining the global prior with local constraints improves reconstruction accuracy.

The method performs well on both phantom and real CT images.

Abstract

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.