  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. To validate that the algorithm closely approximates the true object
in the noiseless case, 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. Reconstructions were performed across a sweep of two primary
design parameters. The results demonstrate that the algorithm recovers the
object in a noiseless simulation case. While the algorithm assumes a specific
blurring model, the results suggest that the algorithm may provide high
reconstruction accuracy even when the object does not match the assumed
blurring model. Generally, increased values of the blurring parameter and TV
weighting parameters reduced noise and streaking artifacts, while decreasing
spatial resolution. As the number of views 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.
