A practical local tomography reconstruction algorithm based on known subregion

TL;DR

This paper introduces a practical local tomography reconstruction method that refines initial images by correcting low frequency artifacts using a Gaussian basis and known subregion constraints, improving accuracy.

Contribution

A novel local tomography reconstruction algorithm utilizing Gaussian basis correction and known subregion constraints for unbiased results.

Findings

The method effectively reduces cupping artifacts.

Using a known subregion ensures unbiased reconstruction.

Simulations confirm the approach's validity.

Abstract

We propose a new method to reconstruct data acquired in a local tomography setup. This method uses an initial reconstruction and refines it by correcting the low frequency artifacts known as the cupping effect. A basis of Gaussian functions is used to correct the initial reconstruction. The coefficients of this basis are iteratively optimized under the constraint of a known subregion. Using a coarse basis reduces the degrees of freedom of the problem while actually correcting the cupping effect. Simulations show that the known region constraint yields an unbiased reconstruction, in accordance to uniqueness theorems stated in local tomography.