Iterative reconstruction methods have become very popular and show the potential to reduce dose. We present a limit to the maximum dose reduction possible with new reconstruction algorithms obtained by analyzing the information content of the raw data, assuming the reconstruction algorithm does not have a priori knowledge about the object or correlations between pixels. This limit applies to the task of estimating the density of a lesion embedded in a known background object, where the shape of the lesion is known but its density is not. Under these conditions, the density of the lesion can be estimated directly from the raw data in an optimal manner. This optimal estimate will meet or outperform the performance of any reconstruction method operating on the raw data, under the condition that the reconstruction method does not introduce a priori information. The raw data bound can be compared to the lesion density estimate from FBP in order to produce a limit on the dose reduction possible from new reconstruction algorithms. The possible dose reduction from iterative reconstruction varies with the object, but for a lesion embedded in the center of a water cylinder, it is less than 40%. Additionally, comparisons between iterative reconstruction and filtered backprojection are sometimes confounded by the effect of through-slice blurring in the iterative reconstruction. We analyzed the magnitude of the variance reduction brought about by through-slice blurring on scanners from two different vendors and found it to range between 11% and 48%.