Comprehensive data set to include interference effects in Monte Carlo models of x-ray coherent scattering inside biological tissues.

Interference effects are included in the x-ray coherent scattering models used in Monte Carlo codes by modifying each material form factor through a proper interference function, which is obtained directly from the measured scattering pattern. This approach is effective for non-biological materials, but it is impractical for biological tissues, due the wide composition variability they can feature. Instead, a given biological sample can be considered as a proper mixture of four basis materials: fat, water, collagen and calcium hydroxyapatite. The sample form factor can then be obtained through a weighted mean of the form factors of the basis materials, which include interference effects. Here, we fully demonstrate the validity of the proposed segmentation method by applying it to 31 biological tissue samples whose form factors are available in the literature. The segmentation, namely the determination of the optimal weight of the basis components, was carried out through a multiple linear regression or, in some cases, by using a controlled trial and error sequence. The form factors of the basis materials were extracted from previous works and elaborated to include more scattering features. In particular, they were interpolated at a denser grid. Furthermore, the data measured separately in wide angle and small angle regimes, for fat and collagen, were merged. In general, a very good agreement was obtained between the original sample and the calculated mixture, being the mean relative difference of their scattering profiles and their attenuation coefficients ∼10%. The segmentation method is fully supported by our extension to the Geant4 model of x-ray coherent scattering, which was used to compare simulated scatter distributions with known experimental data. The developed Geant4 code and a series of molecular form factors, including those of the basis materials, are freely downloadable from a dedicated web repository.