A persistent topic in diffuse optical imaging has been how best to design source and detector configurations. Although it is always possible to simulate data with different source-detector configurations and evaluate the image reconstruction results according to various metrics (e.g., bias and variance,^{1} mutual information^{2}), doing so can be very time- and resource-intensive. For these reasons, there has been enduring interest in methods that allow one to optimize the design of these configurations without having to repeatedly solve the inverse problem. One such approach that has proven popular is singular value analysis (SVA), which involves computing the singular value spectrum of the Jacobian (sensitivity matrix of the forward problem) associated with a particular system configuration and counting the number of singular values above a given threshold.^{3}^{–}^{9} While SVA is conceptually straightforward, it does not make use of the information contained in the singular vectors of the Jacobian, which can lead to errors in performance predictions.^{10}^{,}^{11} It also does not exploit knowledge of the covariance of the measurements. In part to address these shortcomings, some have proposed using the Cramér–Rao lower bound (CRLB) as an alternative design guide.^{12}^{–}^{14} The CRLB defines the best achievable precision of any estimator for a given data model and has long been used in the statistical signal processing community—especially in the radar and sonar signal processing communities—to perform feasibility studies and system design. Like SVA, computing the CRLB does not require solving the inverse problem and should therefore be independent of the method selected to do this in practice. But unlike SVA, which can give only a relative indication of system performance overall, the CRLB can in principle yield numerical values of precision as a function of parameters of interest (e.g., inhomogeneity depth). Although other metrics have been recently proposed to optimize the design of data collection strategies for diffuse optical (DOT)^{15} and fluorescence-mediated tomography (FMT),^{16} to our knowledge only the CRLB can potentially provide the significant advantage of quantitative estimates of performance. We note that despite yielding a lower bound on theoretically available precision, the CRLB does not guarantee that any particular image reconstruction algorithm will be able to reach or even approach the bound. However, in many settings, including the diffuse optical imaging work cited above, differences in the CRLB are used as a surrogate measure of potential reconstruction accuracy. Thus, to be useful for system design, the CRLB should predict performance trends of reasonable reconstruction algorithms as imaging configurations vary.