A method for locally approximating regularized iterative tomographic reconstruction methods

TL;DR

This paper introduces a local approximation method for regularized iterative tomographic reconstructions that reduces computation time by focusing only on regions of interest, maintaining high accuracy compared to global methods.

Contribution

The authors propose a novel local approximation technique for regularized iterative tomography that enables efficient reconstruction of specific regions without reconstructing the entire image.

Findings

Local reconstructions are nearly identical to global methods.

The method significantly reduces computational requirements.

Parallel reconstruction of multiple regions is feasible and efficient.

Abstract

In many applications of tomography, the acquired projections are either limited in number or contain a significant amount of noise. In these cases, standard reconstruction methods tend to produce artifacts that can make further analysis difficult. Advanced regularized iterative methods, such as total variation minimization, are often able to achieve a higher reconstruction quality by exploiting prior knowledge about the scanned object. In practice, however, these methods often have prohibitively long computation times or large memory requirements. Furthermore, since they are based on minimizing a global objective function, regularized iterative methods need to reconstruct the entire scanned object, even when one is only interested in a (small) region of the reconstructed image. In this paper, we present a method to approximate regularized iterative reconstruction methods inside a (small) region of the scanned object. The method only performs computations inside the region of interest, ensuring low computational requirements. Reconstruction results for different phantom images and types of regularization are given, showing that reconstructions of the proposed local method are almost identical to those of the global regularized iterative methods that are approximated, even for relatively small regions of interest. Furthermore, we show that larger regions can be reconstructed efficiently by reconstructing several small regions in parallel and combining them into a single reconstruction afterwards.