In diagnostic radiology, the use of ionizing radiation is justified by benefits surpassing risks. From an epidemiological perspective, this balance is difficult to assess because accurate dose values for individuals are not available. This project consists in developing tools to automatically report dose-to-organs from Computed Tomography (CT) images. First, a machine-learning based, multiclass segmentation tool will be developed to automatically contour organs in CT imaging studies. Then, a fast GPU-based Monte Carlo code will be used to compute dose maps from technical scanning parameters stores in DICOM headers of medical images. A large database of dose-to-organ values will be constituted as well as interactive dashboards to explore dose usage as a function of site explored, device used, etc.

On the long term, this database will be linked with epidemiological cancer data to assess potential causal relations.