This is a review paper on some of the physics, modeling, and iterative algorithms in proton computed tomography (pCT) image reconstruction. The primary challenge in pCT image reconstruction lies in the degraded spatial resolution resulting from multiple Coulomb scattering within the imaged object. Analytical models such as the most likely path (MLP) have been proposed to predict the scattered trajectory from measurements of individual proton location and direction before and after the object. Iterative algorithms provide a exible tool with which to incorporate these models into image reconstruction. The modeling leads to a large and sparse linear system of equations that can eciently be solved by projection methods-based iterative algorithms. Such algorithms perform projections of the iterates onto the hyperlanes that are represented by the linear equations of the system. They perform these projections in possibly various algorithmic structures, such as block-iterative projections (BIP), string-averaging projections (SAP). These algorithmic schemes allow exibility of choosing blocks, strings, and other parameters. They also cater for parallel implementations which are apt to further save clock time in computations. Experimental results are presented which compare some of those algorithmic options.
Sensing and Imaging, accepted for publication.