Multiple linear regression models for individualized radiation exposure planning in dark-field chest radiography

Background: Clinical X-ray dark-field radiography has shown to be promising for visualizing different lung pathologies. To keep the radiation dose as low as reasonably achievable (ALARA principle), individualized exposure planning is necessary. However, the current scanning-based implementation of dark-field radiography complicates the use of automatic exposure control. Previously, a BMI-based linear regression model was proposed as a substitute. Here, we aim to improve this proposed model by investigating multiple linear regression for patient-individual exposure planning of dark-field chest radiography. Methods: For this retrospective study, 273 posteroanterior thorax images acquired at a prototype system for dark-field chest radiography were analyzed retrospectively regarding the X-ray tube current needed to achieve the target radiation dose. Different multiple linear regression models were tested to find the optimal multiple regression model for predicting the necessary tube current based on a person’s weight, height, age, and sex. R² score, the root mean square error (RMSE), and the mean absolute percentage error (MAPE) were used to evaluate the goodness-of-fit of different regression models. Each model was also compared to a BMI-based model. Results: To predict the target tube current for dark-field chest radiography, multiple linear regression using ordinary least squares performed best (R² = 0.712, RMSE = 0.234, and MAPE = 0.033). In comparison, simple linear regression using only the body mass index achieved only R² = 0.627, RMSE = 0.266, and MAPE = 0.037. Conclusion: Multiple linear regression allows better exposure planning in X-ray dark-field chest radiography than simple linear regression.