Synthetic image data play an important role in the verification of medical and biomedical image analysis algorithms. However, the usage of such data strongly relies on their quality and plausibility. Despite the emergence of many frameworks for image synthesis in recent years, the quality of the generated images has not been sufficiently assessed in many cases, or the methodology varied across the publications. If we want to use synthetic image data for the verification of biomedical analysis tools, then the images should resemble the real ones as much as possible with evidence about their similarity. Initially, the hardware available for simulations was limited. Therefore, the validation was not under the scope of interest. With the technological improvements, the expectations put on synthetic data have arisen. Proper validation of synthetic image data is nowadays becoming essential.