Determination of MC-based predictive models for personalized and fast kV-CBCT organ dose estimation

International audiencePurpose or Objective: Monte Carlo (MC) simulations were shown to be a powerful tool to calculate accurately 3D dose distributions of kV-CBCT scans for a patient, based on planning CT images. However, this methodology is still heavy and time consuming, preventing its large use in clinical routine. This study hence explores a method to derive empirical functions relating organ doses to patient morphological parameters, in order to perform a fast and personalized estimation of doses delivered to critical organs by kV-CBCT scans used in IGRT protocols.Material and Methods: Doses to critical organs were first computed using a PENELOPE-based MC code previously validated [H. Chesneau et al., ESTRO 2016], for a set of fifty clinical cases (40 children and 10 adults) covering a broad range of anatomical localizations (head-and-neck, pelvis, thorax, abdomen) and scanning conditions for the Elekta XVI CBCT. Planning CT images were converted into voxellized patient geometries, using a dedicated tissue segmentation procedure: 5 to 7 biological tissues were assigned for soft tissues, whereas ten different bone tissues were required for accurate dosimetry in the kV energy range. Correlations between calculated mean organ doses and several morphological parameters (age, weight, height, BMI, thorax and hip circumference …) were then studied for each anatomical localization to derive appropriate empirical fitting functions.Results: As expected, results on the paediatric cohort show dose variations highly correlated with the patient morphology, varying in the range 3:1 between a 17-y old teenager and a 2-y old baby, for the same CBCT scan. Except for the head-and-neck localization, for which the mean organ doses show no significant variations with the morphology, doses to all major organs at risk can be predicted using linear or exponential functions for thorax, pelvis and abdomen scans. The use of morphological parameters directly measured on the planning CT allows to reach better correlations than global parameters such as BMI, because they represent most relevant indicators of the patient morphology at the scan time.Conclusion: This study demonstrates that it is possible to derive mathematical models predicting the doses delivered to major critical organs by kV-CBCT scans according to morphological parameters. This method allows a fast and personalized estimation of imaging doses usable in clinicalroutine