In the context of investigating the potential of low-dose PET imaging for screening applications, we developed methods to assess small lesion detectability as a function of the number of counts in the scan. We present here our methods and preliminary validation using tuberculosis cases. FDG-PET data from seventeen patients presenting diffuse hyper-metabolic lung lesions were selected for the study, to include a wide range of lesion sizes and contrasts. Reduced doses were simulated by randomly discarding events in the PET list mode, and ten realizations at each simulated dose were generated and reconstructed. The data were grouped into 9 categories determined by the number of included true events, from >40 M to <250 k counts. The images reconstructed from the original full statistical set were used to identify lung lesions, and each was, at every simulated dose, quantified by 6 parameters: lesion metabolic volume, lesion-to-background contrast, mean lesion tracer uptake, standard deviation of activity measurements (across realizations), lesion signal-to-noise ratio (SNR), and Hotelling observer SNR. Additionally, a lesion-detection task including 550 images was presented to several experienced image readers for qualitative assessment. Human observer performances were ranked using receiver operating characteristic analysis. The observer results were correlated with the lesion image measurements and used to train mathematical observer models. Absolute sensitivities and specificities of the human observers, as well as the area under the ROC curve, showed clustering and performance similarities among images produced from 5 million or greater counts. The results presented here are from a clinically realistic but highly constrained experiment, and more work is needed to validate these findings with a larger patient population.