A fast algorithm for high order total variation minimization based interior tomography

Abstract

Interior tomography as a promising X-ray imaging technique has received increasing attention in medical imaging field. In our previous works, we proposed a high-order total variation (HOT) minimization method for interior tomography and proved that the region of interest (ROI) can be reconstructed accurately by minimizing the HOT if the object image is piecewise polynomial within the ROI. In this paper, we propose a modified HOT (MHOT) and develop a fast MHOT minimization algorithm for interior tomography, based on split Bregman iteration and ordered-subset simultaneous algebraic reconstruction techniques (OS-SART). Numerical simulation demonstrates that our algorithm is computationally efficient and can be applied to obtain high-quality reconstructed image.