The image space reconstruction algorithm (ISRA) was proposed as a modification of the expectation maximization (EM) algorithm based on physical considerations for application in volume emission computed tomography. As a consequence of this modification, ISRA searches for least squares solutions instead of maximizing Poisson likelihoods as the EM algorithm. In this paper, we show that both algorithms may be obtained from a common mathematical framework. We use this fact to extend ISRA for penalized likelihood estimates.