Speaker
Description
Modern proton Computed Tomography (pCT) images are usually reconstructed by the algebraic reconstruction techniques (ART). The Kaczmarz-method and its variations are among the most used methods, which are iterative solution techniques for linear problems with sparse matrices. One can ask, whether statistically-motivated iterations, which have been successfully used for emission tomography, can be applied to reconstruct pCT images as well. In my research, I developed a method, based on the Richardson–Lucy deconvolution -- as a statistically-motivated fixed point iteration. I implemented this algorithm to a parallel GPU code, 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.
