Edge-detecting operator-based selection of Huber regularization threshold for low-dose computed tomography imaging / 南方医科大学学报

ABSTRACT

<p><b>OBJECTIVE</b>To compare two methods for threshold selection in Huber regularization for low-dose computed tomography imaging.</p><p><b>METHODS</b>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.</p><p><b>RESULTS</b>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.</p><p><b>CONCLUSION</b>Both of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image iterative reconstruction.</p>