Analysis reveals improved radiation dose prediction in CT scans, indicating a practical approach in clinical settings.
Computed tomography (CT) is a widely used diagnostic tool, but variations in patient anatomy make accurate radiation dose estimation challenging. While size-specific dose estimates (SSDE) can serve as a more effective tool for dose prediction than conventional indices like CTDI vol and DLP, calculating the water-equivalent diameter (D w ) for each slice is time-consuming and often impractical. To address this, we propose a novel method, the SSDE slice-averaged image (SSDE SAI ), which calculates D w from a single image generated by averaging CT values across all slices. This approach captures anatomical variability while reducing calculation effort. We retrospectively analyzed CT data from 282 adult patients in three scan regions: chest, abdomen–pelvis, and chest–abdomen–pelvis (CAP). SSDE SAI was compared with SSDE center and the reference mean SSDE using regression analysis and root mean square error (RMSE). SSDE SAI showed stronger agreement with mean SSDE than SSDE center across all regions, achieving R 2 values up to 0.991 and lower RMSE. These results suggest that SSDE SAI is a more advanced approach for dose prediction and may serve as a practical alternative for routine clinical use.
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Dendo et al. (2025) studied this question.
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