Myocardial blood flow (MBF) measurement has been shown to add incremental value in the diagnosis and prognosis of patients with coronary artery disease. New cardiac cameras make dynamic imaging possible with SPECT and there has been substantial interest in SPECT MBF. As with any new clinical test, a need exists to validate its results, characterize its limitations, and benchmark its performance with evolving methodology. As a test moves into clinical implementation there is a further need to ensure the quality, both of the camera performance in the acquisition of the data and of the processing of that data. Meeting these needs can be readily achieved if there exists a well-controlled reference truth that realistically emulates the inputs to the test.For conventional gamma cameras, there already exist standards for assuring camera performance with respect to static imaging, 1 but many of these tests are not applicable to the new cardiac SPECT camera designs. In addition, as we move into dynamic imaging, there are even less standards available to test and assure accurate performance. From work with PET, we know that camera technical factors such as deadtime corrections 2 and type of reconstruction 3 can influence the values measured for MBF. Therefore, we need to develop new standards for characterizing camera performance, to understand how camera performance influences MBF measurement and to ensure accurate camera performance over its useful life. The ideal test will efficiently test the performance of the complete system, not just select components, by simulating its input and validating the final result.When asking questions about the capability of the design of a system, computer simulations can be extremely valuable. Complex anthropomorphic computer phantoms (such as the XCAT phantom) 4 are available to simulate time-varying activity distributions in the patient, complete with respiratory motion, cardiac contraction and myocardial defects. Coupling these with an accurate model of the camera itself, Monte Carlo simulation (e.g. with GATE 5 or Simind) 6 can provide very realistic datasets for which the truth is well known. Computer simulations also have the advantage that with them we can alter reality-we can turn-off patient breathing, remove scatter or attenuation, increase the tracer extraction fraction-and examine each aspect of the problem and the effect it might have on the final measurement of MBF. However, despite the benefits of computer modeling and the wealth of information it might provide on the ideal performance of a system, it can only simulate what the operator designs into it and there is often an element of doubt that the simulation is accurately modeling all aspects of the problem, especially if complete knowledge of the system (i.e. camera hardware and software) is not known. In addition, and perhaps more importantly, computer simulations can tell us how well the camera design works but cannot tell us how well the camera in our department works.To assess your camera's performance, you need to...