Resolution and noise performance of sparse view X-ray CT reconstruction via Lp-norm regularization

Abstract

ObjectivesAdaptive steepest descent projection onto convex sets (ASD-POCS) algorithms with Lp-norm (0 < p ≤ 1) regularization have shown great promise in sparse-view X-ray CT reconstruction. However, the difference in p value selection can lead to varying algorithm performance of noise and resolution. Therefore, it is imperative to develop a reliable method to evaluate the resolution and noise properties of the ASD-POCS algorithms under different Lp-norm priors. MethodsA comparative performance evaluation of ASD-POCS algorithms under different Lp-norm (0 < p ≤ 2) priors were performed in terms of modulation transfer function (MTF), noise power spectrum (NPS) and noise equivalent quanta (NEQ). Simulation data sets from the EGSnrc/BEAMnrc Monte Carlo system and an actual mouse data set were used for algorithms comparison. ResultsA considerable MTF improvement can be achieved with the decrement of p. L1 regularization based algorithm obtains the best noise performance, and shows superiority in NEQ evaluation. The advantage of L1-norm prior is also confirmed by the reconstructions from the actual mouse data set through contrast to noise ratio (CNR) comparison. ConclusionAlthough the ASD-POCS algorithms using small Lp-norm (p ≤ 0.5) priors yield a higher MTF than do the high Lp-norms, the best noise-resolution performance is achieved when p is between 0.8 and 1. The results are expected to be a reference to the choice of p in Lp-norm (0 < p ≤ 2) regularization.