The Bayesian ideal observer (IO) has been widely advocated to guide hardware optimization. However, except for special cases, computation of the IO test statistic is computationally burdensome and requires an appropriate stochastic object model that may be difficult to determine in practice. Modern reconstruction methods, referred to as sparse reconstruction methods, exploit the fact that objects of interest typically possess sparse representations and have proven to be highly effective at reconstructing images from under-sampled measurement data. Moreover, in computed imaging approaches that employ compressive sensing concepts, imaging hardware and image reconstruction are innately coupled technologies. In this work, we propose a sparsity-driven IO (SD-IO) to guide the optimization of data acquisition parameters for modern computed imaging systems. The SD-IO employs a variational Bayesian inference method to estimate the posterior distribution and calculates an approximate likelihood ratio analytically as its test statistic. Since it assumes knowledge of low-level statistical properties of the object that are related to sparsity, the SD-IO exploits the same statistical information regarding the object that is utilized by highly effective sparse image reconstruction methods. Preliminary simulation results are presented to demonstrate the feasibility of the SD-IO calculation.