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A multivariate approach based on Principal Component Analysis (PCA) was used to analyze the energy distribution of n Monte Carlo simulated Compton scattered photon spectra describing the electron density of large layers. Three to five layers with different density distribution were used to test the algorithm; each layer was obtained by collecting 25 Compton spectra coming from sensitive volumes (SVs) where the complementary conditions of high and low density were realized (respectively full and void SV). The density variation inside a single layer is described by a two principal components (PCs) linear model that depicts the electron density of each SV: the layer density distribution appears to be correctly described even in the presence of very low signal-to-noise Compton spectra. Density profiles for layers at different depths were comparatively analyzed in order to show that, at least within one mean-free-path distance, it is possible to describe the layer density distribution by the PCA without any correction for the beam attenuation.

The suitability of introducing molecular interference effects into coherent scattering phenomenology is considered here in the case of X and gamma photon transport in biological tissues. In order to make its implementation practicable in most of the existing open user Monte Carlo codes, a self-consistent basic set of tabulations for the linear differential scattering coefficient was proposed. The tabulations were critically reviewed by testing their validity over the entire range of the momentum transfer variable from 0 to 10(10) nm(-1). Implementation in three different Monte Carlo codes was performed, and they proved to be reliable.

Photon scattering cross-section data for freshly excised and filtered liquid pig fat was measured in the interval chi = 0.02 to 0.64 A-1 (chi = E*[sin(theta/2)]/12.4; E being the photon incident energy (keV) and theta the scattering angle). The experimental results demonstrate that the marked intermolecular effects of coherent scattering in the forward direction can be exploited as a tool for characterizing lean and fatty tissue.