Enhancing Image Quality While Reducing Dose: A Phantom Study on 100 kVp CT Protocol with iDose
Year: 2025
Type: article
Abstract: This study evaluates how effectively an optimized CT imaging technique can lower patient radiation exposure while preserving approximate diagnostic image quality using a standardized phantom model (ACR CT). On a Philips Access CT scanner, two distinct scanning configurations were used: an optimized method employing 100 kVp in conjunction with sophisticated iterative image reconstruction, and a traditional method using 120 kVp and filtered back projection (FBP). The American College of Radiology (ACR) phantom was the main model used to evaluate important imaging characteristics such signal uniformity, contrast differentiation, and spatial-definition. The volume CT dose index (CTDIvol), dose-length product (DLP), and estimated effective dose were among the dose indications used to evaluate radiation exposure. Every acquisition was completed in duplicate to ensure uniformity. the data was analytically processed using SPSS software (version 27), and findings with p-values less than 0.05 were considered statistically significant in addition to In comparison to the standard protocol, the optimized low-dose protocol (100 kVp, iDose⁴) showed a considerable reduction in radiation dosage, with CTDIvol falling by 29.8% and the effective dose by 27.4% (p < 0.001). There were no statistically significant variations in the procedures' spatial resolution, homogeneity, or CT number accuracy, even with the low dosage settings. Noise reduction and CNR were finally greatly enhanced using the iDose reconstruction technique. The results validate that a low-dose protocol that is tuned and uses iDose⁴ reconstruction reduces radiation dose significantly while maintaining picture quality that is clinically acceptable. Its application in clinical practice to improve patient safety and operational efficiency is supported by phantom-based validation
Language: English
Topic: Radiation Dose and Imaging
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7090459052
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