Article

Evaluation of scatter-to-primary ratio, grid performance and normalized average glandular dose in mammography by Monte Carlo simulation including interference and energy broadening effects.

Departamento de Física e Matemática, FFCLRP, Universidade de São Paulo, 14040-901, Ribeirão Preto, São Paulo, Brazil.
Physics in Medicine and Biology (Impact Factor: 2.7). 08/2010; 55(15):4335-59. DOI:10.1088/0031-9155/55/15/010
Source: PubMed

ABSTRACT 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.

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