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Software breast phantoms are central to the optimization of breast imaging, where in many cases the use of real images would be inefficient – or impossible. Establishing the realism of such phantoms is critical. For this study, patches of simulated breast tissue with different composition – fatty, scattered, heterogenous and dense tissue – were generated using a method based on Perlin noise. The composition of the patches is controlled by numerical parameters derived from input by radiologists and medical physicists with experience of breast imaging. Separate Perlin noise-based methods were used to simulate skin pores, high-frequency noise (representing quantum and electronic noise) and ligaments and vascular structures. In a forced choice reading study, the realism of the simulated tissue patches compared to patches from real mammograms was determined. Patches of 200-500 pixels were extracted from radiolucent, linear, nodular or homogenous (10 per category) mammograms randomly selected from a previously acquired dataset. Eighteen simulated patches in the same size range were added. Four readers, two radiologists and two medical physicists were shown the images in random order and asked to rate them as real or simulated. All readers accepted a substantial fraction of simulated images as real, ranging from 22% to 72%. Only two readers showed a significant difference in the number of images rated real in the real and simulated groups, 22% vs 73% (P= .0003) and 33% vs 63% (P=.04), respectively. These results suggest that the method employed can create images that are almost indistinguishable from patches of real mammograms.
Dustler et al. (Sat,) studied this question.