Quantitative Evaluation of Scatter Correction in 128‑slice Fan‑Beam Computed Tomography Scan using Geant4 Application for Tomographic Emission Monte Carlo Simulation

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Background

Simulation of tomographic imaging systems with fan‑beam geometry, estimation of scattered beam profile using Monte Carlo techniques, and scatter correction using estimated data have always been new challenges in the field of medical imaging. The most important aspect is to ensure the results of the simulation and the accuracy of the scatter correction. This study aims to simulate 128‑slice computed tomography (CT) scan using the Geant4 Application for Tomographic Emission (GATE) program, to assess the validity of this simulation and estimate the scatter profile. Finally, a quantitative comparison of the results is made from scatter correction.

Methods

In this study, 128‑slice CT scan devices with fan‑beam geometry along with two phantoms were simulated by GATE program. Two validation methods were performed to validate the simulation results. The data obtained from scatter estimation of the simulation was used in a projection‑based scatter correction technique, and the post-correction results were analyzed using four quantities, such as: pixel intensity, CT number inaccuracy, contrast‑to‑noise ratio (CNR), and signal‑to‑noise ratio (SNR).

Results

Both validation methods have confirmed the appropriate accuracy of the simulation. In the quantitative analysis of the results before and after the scatter correction, it should be said that the pixel intensity patterns were close to each other, and the accuracy of the CT scan number reached <10%. Moreover, CNR and SNR have increased by more than 30%–65% respectively in all studied areas.

Conclusion

The comparison of the results before and after scatter correction shows an improvement in CNR and SNR while a reduction in cupping artifact according to pixel intensity pattern and enhanced CT number accuracy

Language:
English
Published:
Journal of Medical Signals and Sensors, Volume:13 Issue: 4, Oct-Dec 2023
Pages:
280 to 289
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