Modelling of energy-dependent spectral resolution for SPECT Monte Carlo simulations using SIMIND

Purpose: Monte Carlo (MC) modelling techniques have been used extensively in Nuclear Medicine (NM). The theoretical energy resolution relationship (proportional to 1/root E), does not accurately predict the gamma camera detector response across all energies. This study aimed to validate the accuracy of an energy resolution model for the SIMIND MC simulation code emulating the Siemens Symbia T16 dual-head gamma camera. Methods: Measured intrinsic energy resolution data (full width half maximum (FWHM) values), for Ba-133, Lu-177, Am-241, Ga-67, Tc-99m, I-123, I-131 and F-18 sources in air, were used to create a fitted model of the energy response of the gamma camera. Both the fitted and theoretical models were used to simulate intrinsic and extrinsic energy spectra using three different scenarios (source in air; source in simple scatter phantom and a clinical voxel based digital patient phantom). Results: The results showed the theoretical model underestimated the FWHM values at energies above 160.0 keV up to 23.5 keV. In contrast, the fitted model better predicted the measured FWHM values with differences less than 3.3 keV. The I-131 in-scatter energy spectrum simulated with the fitted model better matched the measured energy spectrum. Higher energy photopeaks, (I-123: 528.9 keV and I-131: 636.9 keV) simulated with the fitted model, more accurately resembled the measured photopeaks. The voxel-based digital patient phantom energy spectra, simulated with the fitted and theoretical models, showed the potential impact of an incorrect energy resolution model when simulating isotopes with multiple photopeaks. Conclusion: Modelling of energy resolution with the proposed fitted model enables the SIMIND user to accurately simulate NM images. A great improvement was seen for high-energy photon emitting isotopes (e.g. I-131), as well as isotopes with multiple photopeaks (e.g. Lu-177, I-131 and Ga-67) in comparison to the theoretical model. This will result in accurate evaluation of radioactivity quantification, which is vital for dosimetric purposes.