Impact of photon cross section uncertainties on Monte Carlo-determined depth-dose distributions

Impact of photoelectric cross section data on systematic uncertainties for Monte Carlo breast dosimetry in mammography

Monte Carlo (MC) simulations are employed extensively in breast dosimetry studies. In the energy interval of interest in mammography energy deposition is predominantly caused by the photoelectric effect, and the corresponding cross sections used by the MC codes to model this interaction process have a direct influence on the simulation results. The present work compares two photoelectric cross section databases in order to estimate the systematic uncertainty, related to breast dosimetry, introduced by the choice of cross sections for photoabsorption. The databases with and without the so-called normalization screening correction are denoted as 'renormalized' or 'un-normalized', respectively. The simulations were performed with the PENELOPE/penEasy code system, for a geometry resembling a mammography examination. The mean glandular dose (MGD), incident air kerma (K_air), normalized glandular dose (DgN) and glandular depth-dose (GDD(z)) were scored, for homogeneous breast phantoms, using both databases. The AAPM Report TG-195 case 3 was replicated, and the results were included. Moreover, cases with heterogeneous and anthropomorphic breast phantoms were also addressed. The results simulated with the un-normalized cross sections are in better overall agreement with the TG-195 data than those from the renormalized cross sections; for MGD the largest discrepancies are 0.13(6)% and 0.74(5)%, respectively. The MGD,K_airand DgN values simulated with the two databases show differences that diminish from approximately 10%/3%/6.8% at 8.25 keV down to 1.5%/1.7%/0.4% at 48.75 keV, respectively. For polyenergetic spectra, deviations up to 2.5% were observed. The disagreement between the GDDs simulated with the analyzed databases increases with depth, ranging from -1% near the breast entrance to 4% near the bottom. Thus, the choice of photoelectric cross section database affects the MC simulation results of breast dosimetry and adds a non-negligible systematic uncertainty to the dosimetric quantities used in mammography.

Uncertainty estimation and statistical comparative methodology for mammography x-ray energy spectra

Numerical models are an alternative to measurements of x-ray energy spectra when validated by comparative methods that assess the similarity of experimental and calculated spectra. In this work, we compared x-ray energy spectra using several methodologies and determined the methodology with highest statistical power among them. Experiments and Monte Carlo (MC) simulations were used to generate a set of 65 experimental and simulated x-ray mammography spectra pairs typically used in mammography applications. They were generated using Tungsten and Molybdenum targets and Molybdenum and Rhodium filters. The x-ray beams were transmitted through breast tissue equivalent material (bTEM) plates with different glandularities and thicknesses, and the transmitted beam was detected using solid-state x-ray spectrometry with a Cadmium Telluride (CdTe) diode. The MC simulations used the PENELOPE code. Additional uncertainties, beyond that from counting, were propagated using the MC method. Quantitative comparative methods based on the [Formula: see text] statistics, the first and second half-value layers, the mean energy, the effective energy, and the non-parametric u-test were applied and their specificity (true negative rate) was assessed. The polyenergetic normalized glandular dose (DgNp) to a 6 cm breast of 50/50 glandularity was derived from the spectra. In this work, the [Formula: see text] statistics attained the highest score; therefore, it is the most indicated metric for the x-ray energy spectra comparative evaluations. The contribution of the additional uncertainties was important, being responsible for up to 98% of the spectra total uncertainty and shifting the mean of the evaluated [Formula: see text] to 1.2(1), compatible with its expected value. The use of non-parametric test is discouraged by our results, since it failed to distinguish spectra pairs that resulted in up to 72% discrepant DgNp.