A machine learning-based pipeline for multi-organ/tissue patient-specific radiation dosimetry in CT

For the study, 95 chest CT scans and 85 abdominal CT scans were collected retrospectively. For each CT scan, a personalized Monte Carlo (MC) simulation was carried out. The produced 3D dose distributions and the respective CT examinations were utilized for the development of organ/tissue-specific dose prediction deep neural networks (DNNs). A pipeline that integrates a robust open-source organ segmentation tool with the dose prediction DNNs was developed for the automatic estimation of radiation doses for 30 organs/tissues including sub-volumes of the heart and lungs. The accuracy and time efficiency of the presented methodology was assessed. Statistical analysis (t-tests) was conducted to determine if the differences between the ground truth organ/tissue radiation dose estimates and the respective dose predictions were significant.

The lowest median percentage differences between MC-derived organ/tissue doses and DNN dose predictions were observed for the lung vessels (4.3%), small bowel (4.7%), pulmonary artery (4.7%), and colon (5.2%), while the highest differences were observed for the right lung’s upper lobe (13.3%), spleen (13.1%), pancreas (12.1%), and stomach (11.6%). Statistical analysis showed that the differences were not significant (p-value > 0.18). Furthermore, the mean inference time, regarding the validation cohort, of the developed methodology was 77.0 ± 11.0 s.