Monte Carlo simulation and experimental validation of SPECT systems via energy spectrum decomposition and image profile analysis
Abstract
Background: Accurate modelling and performance evaluation of gamma-ray scintillation detectors are required in single photon emission computed tomography (SPECT) systems to ensure reliable nuclear medicine diagnostics. Monte Carlo simulation is therefore a powerful computational technique used to accurately model the gamma-ray scintillation detectors and photon transport in medical imaging systems. Therefore, the objective of this study is to validate the SIMIND Monte Carlo simulation code using a self-developed peak-detection algorithm for clinical SPECT gamma cameras (Siemens E.Cam and GE Infinia), with a focus on energy-spectrum decomposition and image-profile analysis.
Methods: This study presents a dual-system evaluation of clinical SPECT gamma cameras-the Siemens E.Cam and GE Infinia systems using the SIMIND Monte Carlo simulation code. We assessed collimator and detector-specific SPECT performance metrics, including energy resolution, sensitivity, image evaluation, spatial resolutions, and spectral fidelity for a 99mTc point source and spatial resolution with Co-57 flood source. A custom algorithm was then developed to automatically decompose energy spectra and identify key spectral components, including the photopeak, Compton edge, iodine escape, and lead X-ray peaks. Quantitative methods, such as 2D and 3D image profile analysis and FWHM comparisons, were integrated to validate the simulation against experimental data.
Results: Energy resolution differences ≤ 4% and…
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