| Summary: | Recent advancements in Computed Tomography (CT) imaging technology have significantly enhanced healthcare delivery. However, public concern regarding the safety and health risks associated with increased exposure to ionizing radiation has escalated. In response, this study performs a quantitative analysis comparing the image quality of the standard CT abdomen protocol (P1 protocols) with alternative optimization techniques. Adjustments to standard CT scan parameters, including tube potential (P2 protocols), effective mAs value (P3), and pitch factor (P4, P5, P6 protocols), were made to evaluate the effectiveness of these optimization strategies. A 64-slice CT scanner (Canon, Japan) and a polymethylmethacrylate (PMMA) textural water phantom was utilized in this study. CT dose descriptors, volume weighted CT Dose Index (CTDIvol) and dose-length product (DLP) alongside evaluation parameters such as Hounsfield Unit (HU) number, noise levels, Signal-to-Noise Ratio (SNR), and Contrast-to-Noise Ratio (CNR) were measured and analysed. Furthermore, this research introduced a Figure of Merit (FOM) based on the ratio of image quality parameters (SNR and CNR) to the CTDIvol, establishing a quantitative measure for optimization. A notable reduction in radiation dose—34.85% below the standard protocol (P1)—was observed in protocol P6, attributed to an increased pitch factor. Conversely, increase the effective mAs value has improved SNR and CNR values. The findings reveal that protocols P1 and P2 deliver the highest FOM for soft tissue and fat imaging, respectively. Protocol P1, with its higher attenuation factor, is optimal for abdominal examinations targeting soft tissues. Meanwhile, protocol P2's increase effective mAs value enhances beam intensity, improving CT numbers for fat tissues with their high-density composition. The study also found a strong correlation between FOM based on SNR and CNR values. In conclusion, adjustments to primary CT parameters significantly influence image quality. The FOM serves as an effective tool for measuring the overall performance of image quality, facilitating post-optimization assessment and contributing to the development of safer, more efficient CT imaging protocols. © 2024 Elsevier Ltd
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