<p>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<sub>vol</sub> and DLP, calculating the water-equivalent diameter (D<sub>w</sub>) for each slice is time-consuming and often impractical. To address this, we propose a novel method, the SSDE slice-averaged image (SSDE<sub>SAI</sub>), which calculates D<sub>w</sub> 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<sub>SAI</sub> was compared with SSDE<sub>center</sub> and the reference mean SSDE using regression analysis and root mean square error (RMSE). SSDE<sub>SAI</sub> showed stronger agreement with mean SSDE than SSDE<sub>center</sub> across all regions, achieving R<sup>2</sup> values up to 0.991 and lower RMSE. These results suggest that SSDE<sub>SAI</sub> is a more advanced approach for dose prediction and may serve as a practical alternative for routine clinical use.</p>

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A practical slice averaged image method for precise CT size specific dose estimates

  • Yutaka Dendo,
  • Keisuke Abe,
  • Shu Onodera,
  • Shingo Kayano,
  • Hideki Ota,
  • Kei Takase

摘要

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 CTDIvol and DLP, calculating the water-equivalent diameter (Dw) for each slice is time-consuming and often impractical. To address this, we propose a novel method, the SSDE slice-averaged image (SSDESAI), which calculates Dw 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). SSDESAI was compared with SSDEcenter and the reference mean SSDE using regression analysis and root mean square error (RMSE). SSDESAI showed stronger agreement with mean SSDE than SSDEcenter across all regions, achieving R2 values up to 0.991 and lower RMSE. These results suggest that SSDESAI is a more advanced approach for dose prediction and may serve as a practical alternative for routine clinical use.