Contributiontothedevelopmentofapersonalized dosimetriccalculationtoolinmammography:Theeffectofbreastvolumeandtissuecomposition

Abstract

Mammography is a key method for the early detection and diagnosis of breast cancer, using X-rays as its main imaging technique. However, the use of these highly energetic, ionising radiations poses significant challenges when it comes to evaluating the associated risks. Accurately determining the absorbed dose and mean glandular dose, a parameter directly related to breast geometry and anatomy, which varies among patients, is essential for assessing these risks.

This variability highlights the need for a personalised approach to dosimetricevalua- tion. This dissertation aims to address this critical issue by studying the mean glandular dose as a function of volume and composition, with the aim of adapting to individual patient characteristics. To achieve this, we employed Monte Carlo simulations to model a mammography X-ray tube, focusing on obtaining the phase space file (PSF), and subse- quently simulated a breast phantom with diverse volumes and compositional variations. The results proves the effectiveness of MC simulations in accurately modeling the x- raytube and that breast volume has a negligible impact on the dosimetric outcomes in mammography while the material composition while the material composition effect can reach up to 64%