Described herein are the advantages of using sub-sinograms for single photon emission computed tomography image reconstruction. A sub-sinogram is a sinogram acquired with an entire data acquisition protocol, but in a fraction of the total acquisition time. A total-sinogram is the summation of all sub-sinograms. Images can be reconstructed from the total-sinogram or from sub-sinograms and then be summed to produce the final image. For a linear reconstruction method such as the filtered backprojection algorithm, there is no advantage of using sub-sinograms. However, for nonlinear methods such as the maximum likelihood (ML) expectation maximization algorithm, the use of sub-sinograms can produce better results. The ML estimator is a random variable, and one ML reconstruction is one realization of the random variable. The ML solution is better obtained via the mean value of the random variable of the ML estimator. Sub-sinograms can provide many realizations of the ML estimator. We show that the use of sub-sinograms can produce better estimations for the ML solution than can the total-sinogram and can also reduce the statistical noise within iteratively reconstructed images.