Radiological Physics and Technology最新文献

Accurate body weight measurement is essential for determining the appropriate dose of contrast agent in contrast-enhanced computed tomography (CT) examinations. However, in emergency medicine, obtaining accurate measurements is often challenging, which can lead to over- or underdosing of contrast medium. Therefore, we aimed to develop and evaluate a deep learning model that estimates body weight using chest-abdominal CT scout images, sex, and height. We retrospectively analyzed the data of 763 hospitalized patients whose CT examination dates matched their weight-measurement dates. This dataset included patients with arms positioned alongside the body and those with metallic implants, commonly encountered in emergency medicine. After performing five-fold cross-validation, a deep learning model based on transfer learning with VGG16 was constructed. The following four input combinations were evaluated: (1) scout images alone; (2) scout images with sex; (3) scout images with height; and (4) scout images with sex and height. The percentages of cases with differences between predicted and actual body weights within ± 5 kg were 84.3%, 90.2%, 92.8%, and 90.2% for inputs (1)-(4), respectively. The corresponding mean absolute percentage errors were 4.8%, 4.7%, 4.1%, and 4.0%, respectively. Our method provides a useful tool for estimating body weight in patients of unknown weight, with its accuracy appearing largely unaffected even when the patients had arms positioned alongside the body or possessed metallic implants. Moreover, incorporating sex and height into the scout images further improved the prediction accuracy.

This study assessed the effect of surface-guided radiation therapy (SGRT)-based prepositioning and respiratory coaching on target localization accuracy in lung stereotactic body radiation therapy (SBRT) using deep inspiration breath-holding. Thirty-five patients treated with lung SBRT (September 2022 to March 2025) were classified into three groups: Group A, VOXELAN prepositioning with reproducible respiratory control (≥ 60% setup criteria satisfied); Group B, VOXELAN prepositioning without reproducible control; and Group C, no VOXELAN prepositioning. Cone-beam computed tomography (CBCT) after prepositioning was used to retrospectively assess target localization. The concordance between the VOXELAN setup criteria and CBCT errors (> 5 mm) was analyzed using Fisher's exact test. Positional deviations and rotations were compared among groups using analysis of variance and post-hoc tests. Satisfying the VOXELAN setup criteria significantly correlated with CBCT localization within 5 mm (p = 0.0027). Vertical errors were smaller in Groups A and B than in Group C (p < 0.01), and lateral errors were smaller in Groups A and B than in Group C (p = 0.01 and p < 0.01, respectively). Rotational errors were within ± 1° in all groups, with a significant difference between Groups A and C (p < 0.02). Longitudinal errors were not significantly different between the groups. SGRT-based prepositioning with respiratory coaching improved setup reproducibility and correlation with the internal target position, particularly in the vertical, lateral, and rotational axes. Longitudinal accuracy remained limited, suggesting caution in margin reduction.

This study aimed to develop and validate a simplified, size-specific model for estimating eye lens dose during head CT imaging, addressing the limitations of generalized size-based methods for anatomically peripheral structures. An equation using effective diameter (D_Eff) was derived from Monte Carlo simulations covering head sizes of 9.9-19.5 cm and CTDI_vol values of 7-160 mGy. D_Eff was selected for its direct measurability from axial CT images. Validation was performed using OSL dosimeters in phantoms across three scanner platforms, comparing model estimates with measurements and SSDE values calculated by AAPM Reports 204 and 293.The equation was derived for 120 kV head CT protocols. The final exponential model demonstrated excellent agreement with reference values R² = 0.996, MAE = 0.94 mGy, RMSE = 1.27 mGy). SSDE-based estimates showed larger discrepancies, particularly in smaller head sizes. The model's correction factor exhibited a more gradual decline with increasing D_Eff, accurately reflecting the dose-size relationship of anteriorly located structures. This study presents a practical size-specific approach for eye lens dose estimation in head CT, providing better alignment with reference values than generalized SSDE methods. The model can be readily implemented in clinical workflows, particularly benefiting emergency scenarios requiring rapid individualized dose estimation.