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<p>This study aimed to predict optimal imaging conditions for dynamic chest radiography (DCR) using exposure times derived from auto exposure control (AEC) during chest X-ray (CXR) imaging and to validate their effectiveness against conventional body mass index (BMI)-based protocols. A total of 579 datasets from patients who underwent both CXR and DCR on the same day were analyzed. The relationship between exposure time for CXR and tube current-time product (mAs) in DCR was assessed, and linear regression models were developed for perfusion and ventilation imaging. Using these models, a refined protocol was developed and evaluated based on deviation from the target S-value (3000). The S-value represents the system sensitivity index in general X-ray imaging, and the target value of 3000 was used as a reference. The evaluation was performed using absolute error (AE) from which mean absolute error (MAE) and mean absolute percentage error (MAPE) were calculated. A strong correlation was observed between exposure time for CXR and mAs values of DCR (<i>r</i> = 0.901 for perfusion; <i>r</i> = 0.831 for ventilation). The refined protocol showed significantly lower MAE and MAPE than the conventional protocol, with narrower error distributions and fewer outliers, indicating improved consistency in image quality. The proposed protocol, based on exposure time for CXR, enables stable imaging conditions in DCR regardless of patient body size or condition and is expected to support dose optimization and standardization of image quality.</p>

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Investigation of dynamic chest radiography exposure parameters using static chest radiography conditions

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Yukihiro Nawa,
  • Taku Kuramoto,
  • Yuichi Imai,
  • Yui Kimoto,
  • Takashi Koda,
  • Hiroyuki Tsushima,
  • Shinji Sakai

摘要

This study aimed to predict optimal imaging conditions for dynamic chest radiography (DCR) using exposure times derived from auto exposure control (AEC) during chest X-ray (CXR) imaging and to validate their effectiveness against conventional body mass index (BMI)-based protocols. A total of 579 datasets from patients who underwent both CXR and DCR on the same day were analyzed. The relationship between exposure time for CXR and tube current-time product (mAs) in DCR was assessed, and linear regression models were developed for perfusion and ventilation imaging. Using these models, a refined protocol was developed and evaluated based on deviation from the target S-value (3000). The S-value represents the system sensitivity index in general X-ray imaging, and the target value of 3000 was used as a reference. The evaluation was performed using absolute error (AE) from which mean absolute error (MAE) and mean absolute percentage error (MAPE) were calculated. A strong correlation was observed between exposure time for CXR and mAs values of DCR (r = 0.901 for perfusion; r = 0.831 for ventilation). The refined protocol showed significantly lower MAE and MAPE than the conventional protocol, with narrower error distributions and fewer outliers, indicating improved consistency in image quality. The proposed protocol, based on exposure time for CXR, enables stable imaging conditions in DCR regardless of patient body size or condition and is expected to support dose optimization and standardization of image quality.