Adding tissue variability to digital breast phantoms for mammography and digital breast tomosynthesis simulations

Intra- and inter-patient tissue variability are seldom implemented in digital phantoms for imaging simulations, which can lead to issues when developing and evaluating material differentiation methods. In this work, we evaluated two methods for generating variability in tissue attenuation properties based on measured properties of human tissue. Our goal is to find a sampling method that generates attenuation curves within measured distributions. The first approach parameterizes tissue attenuation curves as a linear combination of aluminum and PMMA. The second approach is based on the Midgley decomposition model, where the attenuation curve is expressed in terms of five coefficients. Attenuation curves were generated by sampling the two- and five-parameter spaces, and they were compared to previous measurements in ex-vivo adipose tissue acquired at 8 , 11, 15, 20 and 30keV. The average differences of the sampled curves relative to the measurements were 1.68% (2-parameter) and 1.31% (5-parameter), and the absolute differences in coefficients of variation were under 2% for both methods. These results indicate that both methods captured the variability present in measured attenuation curves. This study provides preliminary insights into the effectiveness of two methods for adding tissue variability to imaging simulations.