An Algorithm of l ₁-Norm and l ₀-Norm Regularization Algorithm for CT Image Reconstruction from Limited Projection

Area of Science:

• Medical Imaging
• Computational Imaging
• Image Reconstruction

Background:

• L1-norm regularization is a recognized technique for computed tomography (CT) image reconstruction.
• The L0-norm of image gradients quantifies gradient sparsity, crucial for image detail preservation.

Purpose of the Study:

• To develop a new regularization model for CT image reconstruction using limited projection data.
• To combine L1-norm and L0-norm regularization for enhanced reconstruction performance.

Main Methods:

• A novel combined L1-norm and L0-norm regularization model was formulated.
• An optimization algorithm using the nonmonotone alternating direction with hard thresholding method was proposed.
• The algorithm was applied within an algebraic framework for effective solution.

Main Results:

• Numerical experiments demonstrated significant improvements in image reconstruction.
• The inclusion of L0-norm regularization was key to the observed enhancements.
• The proposed algorithm effectively solved the optimization problem.

Conclusions:

• The combined L1-norm and L0-norm regularization model offers superior performance for CT image reconstruction.
• The developed algorithm efficiently reconstructs images from limited projection data.
• This approach advances the field of sparse-regularized CT image reconstruction.