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Previous work showed that total variation superiorization (TVS) improvesreconstructed image quality in proton computed tomography (pCT). The structureof the TVS algorithm has evolved since then and this work investigated if thisnew algorithmic structure provides additional benefits to pCT image quality.Structural and parametric changes introduced to the original TVS algorithmincluded: (1) inclusion or exclusion of TV reduction requirement, (2) avariable number, $N$, of TV perturbation steps per feasibility-seekingiteration, and (3) introduction of a perturbation kernel $0<\alpha<1$. Thestructural change of excluding the TV reduction requirement check tended tohave a beneficial effect for $3\le N\le 6$ and allows full parallelization ofthe TVS algorithm. Repeated perturbations per feasibility-seeking iterationsreduced total variation (TV) and material dependent standard deviations for$3\le N\le 6$. The perturbation kernel $\alpha$, equivalent to $\alpha=0.5$ inthe original TVS algorithm, reduced TV and standard deviations as $\alpha$ wasincreased beyond $\alpha=0.5$, but negatively impacted reconstructed relativestopping power (RSP) values for $\alpha>0.75$. The reductions in TV andstandard deviations allowed feasibility-seeking with a larger relaxationparameter $\lambda$ than previously used, without the corresponding increasesin standard deviations experienced with the original TVS algorithm. This workdemonstrates that the modifications related to the evolution of the originalTVS algorithm provide benefits in terms of both pCT image quality andcomputational efficiency for appropriately chosen parameter values.

An Improved Method of Total Variation Superiorization Applied to Reconstruction in Proton Computed Tomography