Methodology for simulating x-ray sources of computed tomography systems using GATE 10 without manufacturer data

This study aims to implement and evaluate a source modeling method for Monte Carlo (MC) simulation of computed tomography (CT) systems in the absence of manufacturer data. This work enables simulation of realistic CT x-ray sources by integrating experimental measurements, particularly x-ray spectrometry, and to assess its applicability to single-energy CT (SECT) scanners.&#xD;Approach: An experimental method was implemented combining x-ray spectra using a CdTe spectrometer and a bowtie filter attenuation profile measured with an ionization chamber, and compared to manufacturer data. Two source models with different levels of complexity were created for GATE 10 simulations. First, a basic model (Single-spectrum) using one energy spectrum measured on the beam axis combined with the attenuation profile was considered. Multi-spectra model incorporated additional spectra off-axis. For comparison, the Single-spectrum model was used with manufacturer's data. &#xD;These source models were evaluated by comparing simulations and measurements of half-value layers (HVL) of aluminum at three voltages (80 kV, 120 kV and 140 kV), as well as CT dose index (CTDI) values measured on both head and body phantoms. &#xD;Main results: HVL of Single-spectrum and Multi-spectra models showed a better agreement with measurements, yielding a mean difference of 4%. CTDI values derived from simulations based on Multi-spectra source outperformed the other sources, with differences inferior to 7% with measurements. Single-spectrum results had good agreement with measurements (<10%), but underestimated dose evaluation in some cases by up to 16%. The Manufacturer source showed the largest discrepancies, especially at 140 kV.&#xD;Significance: This work highlighted that modeling spectral variations across the x-ray beam significantly improves CT source model. The proposed framework offers a replacement for manufacturer data when not available and participates in the foundation for using Monte Carlo methods to support the integration of new CT systems.