In this work, a computational code for the study of imaging systems and dosimetry in conventional and digital mammography through Monte Carlo simulations is described. The developed code includes interference and Doppler energy broadening for simulation of elastic and inelastic photon scattering, respectively. The code estimates the contribution of scattered radiation to image quality through the spatial distribution of the scatter-to-primary ratio (S/P). It allows the inclusion of different designs of anti-scatter grids (linear or cellular), for evaluation of contrast improvement factor (CIF), Bucky factor (BF) and signal difference-to-noise ratio improvement factor (SIF). It also allows the computation of the normalized average glandular dose, D(g).(N). These quantities were studied for different breast thicknesses and compositions, anode/filter combinations and tube potentials. Results showed that the S/P increases linearly with breast thickness, varying slightly with breast composition or the spectrum used. Evaluation of grid performance showed that the cellular grid provides the highest CIF with smaller BF. The SIF was also greater for the cellular grid, although both grids showed SIF < 1 for thin breasts. Results for D(g).(N) showed that it increases with the half-value layer (HVL) of the spectrum, decreases considerably with breast thickness and has a small dependence on the anode/filter combination. Inclusion of interference effects of breast tissues affected the values of S/P obtained with the grid up to 25%, while the energy broadening effect produced smaller variations on the evaluated quantities.

In this work, a Monte Carlo code was used to investigate the performance of different x-ray spectra in digital mammography, through a figure of merit (FOM), defined as FOM = CNR²/(¯)D(g), with CNR being the contrast-to-noise ratio in image and [Formula: see text] being the average glandular dose. The FOM was studied for breasts with different thicknesses t (2 cm ≤ t ≤ 8 cm) and glandular contents (25%, 50% and 75% glandularity). The anode/filter combinations evaluated were those traditionally employed in mammography (Mo/Mo, Mo/Rh, Rh/Rh), and a W anode combined with Al or K-edge filters (Zr, Mo, Rh, Pd, Ag, Cd, Sn), for tube potentials between 22 and 34 kVp. Results show that the W anode combined with K-edge filters provides higher values of FOM for all breast thicknesses investigated. Nevertheless, the most suitable filter and tube potential depend on the breast thickness, and for t ≥ 6 cm, they also depend on breast glandularity. Particularly for thick and dense breasts, a W anode combined with K-edge filters can greatly improve the digital technique, with the values of FOM up to 200% greater than that obtained with the anode/filter combinations and tube potentials traditionally employed in mammography. For breasts with t < 4 cm, a general good performance was obtained with the W anode combined with 60 μm of the Mo filter at 24-25 kVp, while 60 μm of the Pd filter provided a general good performance at 24-26 kVp for t = 4 cm, and at 28-30 and 29-31 kVp for t = 6 and 8 cm, respectively.

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The angular distribution of photons scattered by breast tissue samples (20 normal and 13 malignant) was measured in the interval of momentum transfer 0.02Å −1 Ä x Ä 0.62Å −1 , using a powder diffractometer apparatus operating in reflection mode and equipped with a monochromator in the scattering beam. The x-ray tube had a Cu anode and added Ni filtration. Experimental data was corrected for all background sources, polarization, self-attenuation and geometric effect in order to provide the linear differential scattering coefficient m s (scattering profile) of each sample. Discriminant analysis was applied to these profiles in order to quantify the differences existing between normal and malignant tissues. In this analysis, each momentum transfer measured was considered as an independent variable, and the scores of each sample on a discriminant function were obtained. The classification method of decision tree induction was applied to these scores, which allowed tissue characterization. The classification results were validated and compared with histological sample classifications obtained by microscopic analyses. The results have shown that discriminant analysis was able to successfully identify differences between the scattering profiles from normal and malignant tissues. Also, classification results allowed the correct identification of 97% of the analyzed samples. These results suggest that the scattering profile may be an effective marker to be used in the diagnosis of breast cancer.

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