Modern proton Computed Tomography (pCT) images are usually reconstructed by algebraic reconstruction techniques (ART). The Kaczmarz-method and its variations are among the most widely used methods, which are iterative solution techniques for linear problems with sparse matrices. It is an interesting question whether statistically-motivated iterations, which have been successfully used for emission tomography, can be applied to reconstruct the novel technology of pCT images as well.
In my research, I developed a method for pCT image reconstruction, based on the Richardson–Lucy deconvolution. It treats the problem as a statistically-motivated fixed-point iteration. I implemented this algorithm as a parallel code to GPU, with spline-based trajectory calculation and on-the-fly system matrix generation. My results presented that the method works well, and it can be successfully applied in pCT applications, such as in the detector R&D of the Bergen pCT Collaboration.