"How much realism is needed?" - the wrong question in silico imagers have been asking

Purpose: To discuss the use of realism as a first approximation for assessing computational imaging methods. Methods: Although in silico methods are increasingly becoming promising surrogates to physical experimentation for various stages of device development, their acceptance remains challenging. Realism is often considered as a first approximation for assessing computational imaging methods. However, realism is subjective and does not always ensure that key features of the methodologies reflect relevant aspects of devices of interest to imaging scientists, regulators, and medical practitioners. Moreover, in some cases (e.g., in computerized image analysis applications where human interpretation is not needed) how realistic in silico images are is irrelevant and perhaps misleading. Results: I emphasize a divergence from this methodology by providing a rationale for evaluating in silico imaging methods and tools in an objective and measurable manner. Conclusions: Improved approaches for in silico imaging will lead to the rapid advancement and acceptance of computational techniques in medical imaging primarily but not limited to the regulatory evaluation of new imaging products. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.