A practical local tomography reconstruction algorithm based on known subregion
This work addresses artifact correction in local tomography, which is incremental as it builds on existing methods with a known subregion constraint.
The paper tackles the problem of reconstructing data in local tomography by correcting low-frequency artifacts known as the cupping effect, using a basis of Gaussian functions and a known subregion constraint, resulting in an unbiased reconstruction as shown in simulations.
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.