Mass spectrometric imaging is a powerful tool to interrogate biological complexity. One such technique, time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging, has been successfully utilized for subcellular imaging of cell membrane components. In order for this technique to provide insight into biological processes, it is critical to characterize the figures of merit. Because a SIMS instrument counts individual events, the precision of the measurement is controlled by counting statistics. As the analysis area decreases, the number of molecules available for analysis diminishes. This becomes critical when imaging subcellular features; it limits the information obtainable, resulting in images with only a few counts of interest per pixel. Many features observed in low intensity images are artifacts of counting statistics, making validation of these features crucial to arriving at accurate conclusions. With TOF-SIMS imaging, the experimentally attainable spatial resolution is a function of the molecule of interest, sample matrix, concentration, primary ion, instrument transmission, and spot size of the primary ion beam. A model, based on Poisson statistics, has been developed to validate SIMS imaging data when signal is limited. This model can be used to estimate the effective spatial resolution and limits of detection prior to analysis, making it a powerful tool for tailoring future investigations. In addition, the model allows comparison of pixel-to-pixel intensity and can be used to validate the significance of observed image features. The implications and capabilities of the model are demonstrated by imaging the cell membrane of resting RBL-2H3 mast cells.