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Effect of scatter correction on quantitative cardiac perfusionspect studies: A Monte Carlo study

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2025
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This thesis explores the effect of scatter correction (SC) techniques on image qual ity in quantitative myocardial perfusion SPECT imaging through a simulation-based ap proach. Using the SIMIND Monte Carlo program and the 4D XCAT phantom, projection data were generated for eight dynamic frames representing different phases of the car diac cycle. The study systematically evaluated two common scatter correction methods: Dual Energy Window (DEW, with k = 0.5) and Triple Energy Window (TEW), applied across various photopeak window widths (2, 4, and 6 keV) and configurations (overlapped and non-overlapped). Images were reconstructed using the CASTOR software with the OSEM algorithm, including three reconstruction conditions: uncorrected (UC), scatter corrected (SC), and both attenuation and scatter corrected (ACSC), with attenuation maps derived from SIMIND. Quantitative image analysis focused on four metrics: signal to-noise ratio (SNR), contrast-to-noise ratio (CNR), defect contrast, and relative noise to background (RNB). Non-parametric statistical tests (Friedman and Wilcoxon signed rank tests) were used due to non-normal data distributions. Results demonstrated that ACSC consistently achieved the highest values in CNR, SNR, and defect contrast across all configurations, confirming its superiority over SC and UC. Notably, SC alone reduced image noise but also led to lower defect contrast compared to UC, indicating a poten tial trade-off when applied without attenuation correction. The influence of photopeak window width and overlap configuration on image quality metrics was minimal, with no statistically significant improvements observed. Furthermore, comparison between DEW and TEW methods revealed no significant differences in most image quality metrics, suggesting both methods yield comparable performance.

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Mounıa El Bab

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Mounıa El Bab (Master Thesis). Effect of scatter correction on quantitative cardiac perfusionspect studies: A Monte Carlo study, 2025, Boğaziçi University.

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