Currently available tomographic image reconstruction schemes for photon migration tomography (PMT) are mostly based on the limiting assumptions of small perturbations and a priori knowledge of the optical properties of a reference medium. In this work a model-based iterative image reconstruction (MOBIIR) method is presented, which does not require the knowledge of a reference medium or that the encountered heterogeneities are small perturbations. The code consists of three major parts: (1) A finite-difference, time-resolved, diffusion forward model is used to predict detector readings based on the spatial distribution of optical properties; (2) An objective function that describes the difference between predicted and measured data; (3) An updating scheme that uses the gradient of the objective function to provide subsequent guesses of the spatial distribution of the optical properties for the forward model. The reconstruction of these properties is completed, once a minimum of this objective function is found. After a review of the previously published mathematical background, the clinically relevant examples of breast cancer detection and brain imaging are discussed. It is shown that cysts and tumors can be distinguished using the MOBIIR technique, even in a heterogeneous background.