Abstract: Objective To compare two methods for threshold selection in Huber regularization for low-dose computed
tomography imaging. Methods Huber regularization-based iterative reconstruction (IR) approach was adopted for low-dose
CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge-detecting
operators. Results The experimental results on the simulation data demonstrated that both of the two threshold selection
methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal. Conclusion
Both of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image
iterative reconstruction.