A First-Order Primal-Dual Reconstruction Algorithm for Few-View SPECT

A sparsity-exploiting algorithm intended for few-view Single Photon Emission Computed Tomography (SPECT) reconstruction is proposed and characterized. The algorithm models the object as piecewise constant subject to a blurring operation. Monte Carlo simulations were performed to provide more projection data of a phantom with varying smoothness across the field of view. For all simulations, reconstructions were performed across a sweep of the two primary design parameters: the blurring parameter and the weighting of the total variation (TV) minimization term. Maximum-Likelihood Expectation Maximization (MLEM) reconstructions were performed to provide reference images. Spatial resolution, accuracy, and signal-to-noise ratio was calculated and compared for all reconstructions. In general, increased values of the blurring parameter and TV weighting parameters reduced noise and streaking artifacts, while decreasing spatial resolution. The reconstructed images demonstrate that the algorithm introduces low-frequency artifacts in some cases, but eliminates streak artifacts due to angular undersampling. Further, as the number of views was decreased from 60 to 9 the accuracy of images reconstructed using the proposed algorithm varied by less than 3%. Overall, the results demonstrate preliminary feasibility of a sparsity-exploiting reconstruction algorithm which may be beneficial for few-view SPECT.