Abstract
Purpose Medical exposures are the main source of artificial exposure to ionizing radiation for developed countries. In recent years there has been a significant improvement in imaging techniques but also a substantial increase of patient exposures. The harmful effects of radiations used for diagnostic purposes are essentially stochastic. Exposure to ionizing radiation is known to increase the future incidence of cancer (solid tumors and leukemias). The latest EU radiation protection recommendations of the Directive 2013/59/EURATOM layied down basic safety standards for exposure to ionizing radiation. The region of Tuscany (through resolution No 994/2014) has planned a project aimed to assess the annual per-capita and collective effective doses due to medical exposures. Methods The total number of examinations was evaluated by data recorded on the Radiological Information System (RIS) of each regional hospital and by the regional database for Outpatient services (SPA) and Hospitalized Patients (SDO). Relevant exposure parameters for effective dose estimation were collected by a questionnaire that allowed a sampling of dosimetry aspects of radiological and nuclear medicine practices throughout the region. These data were used to evaluate the effective dose associated with each examination by simulations with dedicated software. A different approach has been used for interventional radiology. Due to the intrinsic variability characterizing this kind of procedures, the exposure and dosimetric parameters of each exam were extracted from databases implemented by commercially available dose monitoring software packages and then processed with specific simulation software for effective dose estimate. Results We derived per-capita and collective effective dose for the Tuscany Region population due to radiology (computed tomography, conventional radiology, interventional radiology and mammography) and nuclear medicine procedures. Conclusions We compared the current data with those of previous surveys to outline any statistically significant trend.
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