Development and performance of a sparsity-exploiting algorithm for few-view single photon emission computed tomography (SPECT) reconstruction

Single Photon Emission Computed Tomography (SPECT) provides noninvasive images of the distribution of radiotracer molecules. Dynamic Single Photon Emission Computed Tomography provides information about tracer uptake and washout from a series of time-sequence images. Stationary ring-like multi-camera systems are being developed to provide rapid dynamic acquisitions with high temporal sampling. Reducing the number of cameras reduces the cost of such systems but also reduces the number of views acquired, limiting the angular sampling of the system. Novel few-view image reconstruction methods may be beneficial and are being investigated for the application of dynamic SPECT. A sparsity-exploiting algorithm intended for few-view Single Photon Emission Computed Tomography (SPECT) reconstruction is proposed and characterized. The reconstruction algorithm phenomenologically models the object as piecewise constant subject to a blurring operation. To validate that the reconstruction algorithm closely approximates the true object when the object model is known and the system is modeled exactly, projection data were generated from an object assuming this model and using the system matrix. Monte Carlo simulations were performed to provide more realistic 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. A range of noise and angular sampling conditions were also investigated. Maximum-Likelihood Expectation Maximization (MLEM) reconstructions were performed to provide a reference image. Spatial resolution, accuracy, and signal-to-noise ratio were calculated and compared for all reconstructions. The results demonstrate that the reconstruction algorithm very closely approximates the true object under ideal conditions. While this reconstruction technique assumes a specific blurring model, the results suggest that the algorithm may provide high reconstruction accuracy even when the true blurring parameter is unknown. 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 reconstruction algorithm introduces low-frequency artifacts in the presence of noise, 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