Optimizing Patient Safety and Dose Reduction Strategies in Abdominal, Chest, and Skull CT Imaging: A Comprehensive Analysis of Effective Dose Quantification

Williams Igoniye*
Periodicity:July - December'2024

Abstract

CT imaging plays a crucial role in medical diagnostics, but it comes with radiation exposure risks. Diagnostic Reference Levels (DRLs) are essential tools for optimizing imaging protocols and ensuring patient safety. This study evaluates the effective dose (ED) from abdominal, chest, and skull CT exams using advanced MDCT systems across ten radiological centers in southern Nigeria, with the primary objective of assessing the alignment of local imaging protocols with international reference standards to optimize patient safety and reduce radiation exposure. The study quantifies the effective doses for these CT examinations, compares them with national and local DRLs, and analyzes adherence to international benchmarks to identify opportunities for dose reduction and safety improvements. A total of 2,828 CT scans, including abdominal, chest, and skull imaging, were analyzed from MDCT scanners, with dose descriptors extracted from image dose reports to calculate the ED using established dose-length product conversion coefficients. The calculated ED values were compared with national and local DRLs as well as internationally published reference benchmarks. The comparative analysis revealed significant variations in ED values across different types of CT exams, highlighting areas where dose optimization is possible. The findings suggest that some centers need to adjust their protocols to better align with best practices and improve patient safety. This study provides valuable insights into radiation dose levels in southern Nigeria, offering recommendations for protocol adjustments and enhanced patient safety while serving as a guide for improving local imaging practices, reducing radiation exposure, and ensuring better clinical outcomes.

Keywords

Effective dose, Radiation safety, Computed Tomography, Quantifying

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