Assessment of different computational models for generation of x‐ray spectra in diagnostic radiology and mammography

Different computational methods based on empirical or semi-empirical models and sophisticated Monte Carlo calculations have been proposed for prediction of x-ray spectra both in diagnostic radiology and mammography. In this work, the x-ray spectra predicted by various computational models used in the diagnostic radiology and mammography energy range have been assessed by comparison with measured spectra and their effect on the calculation of absorbed dose and effective dose (ED) imparted to the adult ORNL hermaphroditic phantom quantified. This includes empirical models (TASMIP and MASMIP), semi-empirical models (X-rayb&m, X-raytbc, XCOMP, IPEM, Tucker et al., and Blough et al.), and Monte Carlo modeling (EGS4, ITS3.0, and MCNP4C). As part of the comparative assessment, the K x-ray yield, transmission curves, and half value layers (HVLs) have been calculated for the spectra generated with all computational models at different tube voltages. The measured x-ray spectra agreed well with the generated spectra when using X-raytbc and IPEM in diagnostic radiology and mammography energy ranges, respectively. Despite the systematic differences between the simulated and reference spectra for some models, the student's t-test statistical analysis showed there is no statistically significant difference between measured and generated spectra for all computational models investigated in this study. The MCNP4C-based Monte Carlo calculations showed there is no discernable discrepancy in the calculation of absorbed dose and ED in the adult ORNL hermaphroditic phantom when using different computational models for generating the x-ray spectra. Nevertheless, given the limited flexibility of the empirical and semi-empirical models, the spectra obtained through Monte Carlo modeling offer several advantages by providing detailed information about the interactions in the target and filters, which is relevant for the design of new target and filter combinations and optimization of radiological imaging protocols.