PURPOSE: The purpose of this study was to compare and validate three methods to simulate radiographic image detectors with the Monte Carlo software MCNP/MCNPX in a time efficient way. METHODS: The first detector model was the standard semideterministic radiography tally, which has been used in previous image simulation studies. Next to the radiography tally two alternative stochastic detector models were developed: A perfect energy integrating detector and a detector based on the energy absorbed in the detector material. Validation of three image detector models was performed by comparing calculated scatter-to-primary ratios (SPRs) with the published and experimentally acquired SPR values. RESULTS: For mammographic applications, SPRs computed with the radiography tally were up to 44% larger than the published results, while the SPRs computed with the perfect energy integrating detectors and the blur-free absorbed energy detector model were, on the average, 0.3% (ranging from -3% to 3%) and 0.4% (ranging from -5% to 5%) lower, respectively. For general radiography applications, the radiography tally overestimated the measured SPR by as much as 46%. The SPRs calculated with the perfect energy integrating detectors were, on the average, 4.7% (ranging from -5.3% to -4%) lower than the measured SPRs, whereas for the blur-free absorbed energy detector model, the calculated SPRs were, on the average, 1.3% (ranging from -0.1% to 2.4%) larger than the measured SPRs. CONCLUSIONS: For mammographic applications, both the perfect energy integrating detector model and the blur-free energy absorbing detector model can be used to simulate image detectors, whereas for conventional x-ray imaging using higher energies, the blur-free energy absorbing detector model is the most appropriate image detector model. The radiography tally overestimates the scattered part and should therefore not be used to simulate radiographic image detectors.
PURPOSE: The purpose of this study was to compare and validate three methods to simulate radiographic image detectors with the Monte Carlo software MCNP/MCNPX in a time efficient way. METHODS: The first detector model was the standard semideterministic radiography tally, which has been used in previous image simulation studies. Next to the radiography tally two alternative stochastic detector models were developed: A perfect energy integrating detector and a detector based on the energy absorbed in the detector material. Validation of three image detector models was performed by comparing calculated scatter-to-primary ratios (SPRs) with the published and experimentally acquired SPR values. RESULTS: For mammographic applications, SPRs computed with the radiography tally were up to 44% larger than the published results, while the SPRs computed with the perfect energy integrating detectors and the blur-free absorbed energy detector model were, on the average, 0.3% (ranging from -3% to 3%) and 0.4% (ranging from -5% to 5%) lower, respectively. For general radiography applications, the radiography tally overestimated the measured SPR by as much as 46%. The SPRs calculated with the perfect energy integrating detectors were, on the average, 4.7% (ranging from -5.3% to -4%) lower than the measured SPRs, whereas for the blur-free absorbed energy detector model, the calculated SPRs were, on the average, 1.3% (ranging from -0.1% to 2.4%) larger than the measured SPRs. CONCLUSIONS: For mammographic applications, both the perfect energy integrating detector model and the blur-free energy absorbing detector model can be used to simulate image detectors, whereas for conventional x-ray imaging using higher energies, the blur-free energy absorbing detector model is the most appropriate image detector model. The radiography tally overestimates the scattered part and should therefore not be used to simulate radiographic image detectors.