Introduction of Total Variation Regularization into Filtered Backprojection Algorithm

Iterative total-variation reconstruction versus weighted filtered-backprojection reconstruction with edge-preserving filtering.

Iterative image reconstruction with the total-variation (TV) constraint has become an active research area in recent years, especially in x-ray CT and MRI. Based on Green's one-step-late algorithm, this paper develops a transmission noise weighted iterative algorithm with a TV prior. This paper compares the reconstructions from this iterative TV algorithm with reconstructions from our previousl...

On the optimality of the filtered backprojection algorithm.

It is shown that under certain conditions the filtered backprojection algorithm produces a computed tomographic reconstruction for which the statistical accuracy attainable in the amplitude estimation of large-area objects meets the general lower bound derived by Tretiak. In this sense, filtered backprojection is an optimum algorithm.

The Semidiscrete Filtered Backprojection Algorithm Is Optimal for Tomographic Inversion

O(N2log2N) filtered backprojection reconstruction algorithm for tomography

We present a new fast reconstruction algorithm for parallel beam tomography. The new algorithm is an accelerated version of the standard filtered backprojection (FBP) reconstruction, and uses a hierarchical decomposition of the backprojection operation to reduce the computational cost from O(N(3)) to O(N(2)log(2 )N). We discuss the choice of the various parameters that affect the behavior of th...

Total Variation Regularization in

We propose computational algorithms for incorporating total varia-tional (TV) regularization in positron emission tomography (PET). The motivation for using TV is that it has been shown to suppress noise effectively while capturing sharp edges without oscillations. This feature makes it particularly attractive for those applications of PET where the objective is to identify the shape of objects...