# Local Linear Constraint based Optimization Model for Dual Spectral CT

## TL;DR

This paper introduces a novel optimization model for dual spectral CT that leverages local linear constraints to improve image quality and noise robustness, validated through simulations and real data.

## Contribution

It proposes a new local linear constraint-based optimization model for DSCT, enhancing image quality and noise suppression over traditional methods.

## Key findings

- Improved signal-to-noise ratio in decomposed images
- Effective noise reduction demonstrated in experiments
- Enhanced material distinguishability in dual spectral CT

## Abstract

Dual spectral computed tomography (DSCT) can achieve energy- and material-selective images, and has a superior distinguishability of some materials than conventional single spectral computed tomography (SSCT). However, the decomposition process is illposed, which is sensitive with noise, thus the quality of decomposed images are usually degraded, especially the signal-to-noise ratio (SNR) is much lower than single spectra based directly reconstructions. In this work, we first establish a local linear relationship between dual spectra based decomposed results and single spectra based directly reconstructed images. Then, based on this constraint, we propose an optimization model for DSCT and develop a guided image filtering based iterative solution method. Both simulated and real experiments are provided to validate the effectiveness of the proposed approach.