Block-Iterative and String-Averaging Projection Algorithms in Proton Computed Tomography Image Reconstruction

Proton computed tomography (pCT) is an imaging modality that is based on tracking individual protons as they traverse the object to be imaged. Proton-by-proton tracking is necessary due to the effects of multiple Coulomb scattering (MCS), a process that deviates the proton path from a striaght line. If optimal spatial resolution is to be achieved, the path of each proton must be predicted with a maximum likelihood formalism that models MCS. Further, image reconstruction methods are required that are able to handle these non-linear paths. This had led to the exploration of algebraic reconstruction techniques (ART) in pCT. However, because iterative algebraic methods are computationally expensive, parallel compatible versions of the ART class, executed simultaneously over multiple processing units are required if pCT is to be realistic for a clinical environment. In this study we investigate the image quality achieveable with block-iterative and stringaveraging projection algorithms in application to simulated pCT data. From the results we make a recommendation as to which algorithms should be used in future studies with pCT image reconstruction.