Evaluation of Automatically Modulating X-Ray Tube Current Relative to a Constant Reference Tube Current Across Various Energy Settings Influencing Radiation Dose Optimization in Computed Tomography
Keywords:
Computed Tomography (CT); Radiation Dose Optimization; Automatic Exposure Control (AEC); Artificial Intelligence (AI); X-ray Tube Current Modulation; BMI-Based Dose OptimizationAbstract
'Computed Tomography' (CT) is expected to play a crucial role in the field of medical imaging in the future, and combining CT with other imaging techniques, such as PET (molecular imaging), is a key aspect of the development of medical imaging. Computed Tomography (CT) started in the 1970s as a manual process involving single-slice scanning and evolved into automated multi-slice systems by the 1990s. Today, artificial intelligence is used for faster acquisition of scan, reduce radiation exposure, and improve image processing. Although diagnostic CT scans offer multiple benefits, evidence points to radiation-induced risks and Radiation can damage the DNA present in reproductive cells (such as sperm or eggs), which is linked to human genetics., underscoring the importance of optimizing radiation doses. This study focused on the fundamental aspects of computed tomography that influence radiation exposure and to explore the potential of automation and artificial intelligence in radiation dose optimization, this study evaluated a constant-reference X-ray tube current—alongside automated radiation exposure modulation strategies. constant-reference X-ray tube current and time product provides uniform radiation exposure if used without automatic modulation of the X-ray tube current and also serves as the baseline mAs during automatic modulation at various energy settings. In this study, patients were categorized into different groups based on their BMI. To evaluate the effectiveness and limitations of automotive modulation versus non-automative uniform exposure techniques, comparisons were made regarding key facets influencing radiation dose exposure (such as kVp and mAs). Finally, the use and future prospects of automatic methods (including artificial intelligence) for optimizing radiation doses in computed tomography were assessed.





