EPJ Web Conf.
Volume 153, 2017ICRS-13 & RPSD-2016, 13th International Conference on Radiation Shielding & 19th Topical Meeting of the Radiation Protection and Shielding Division of the American Nuclear Society - 2016
|Number of page(s)||4|
|Section||4. Medical Facilities, Radiotherapy & Medical Applications, Space Dosimetry & Shielding|
|Published online||25 September 2017|
Validation of Monte Carlo simulation of mammography with TLD measurement and depth dose calculation with a detailed breast model
1 Department of Engineering Physics, Tsinghua University, Beijing, China
2 Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China
3 Nuctech Company Limited, Beijing, China
a Corresponding author: firstname.lastname@example.org
Published online: 25 September 2017
Mean glandular dose (MGD) is not only determined by the compressed breast thickness (CBT) and the glandular content, but also by the distribution of glandular tissues in breast. Depth dose inside the breast in mammography has been widely concerned as glandular dose decreases rapidly with increasing depth. In this study, an experiment using thermo luminescent dosimeters (TLDs) was carried out to validate Monte Carlo simulations of mammography. Percent depth doses (PDDs) at different depth values were measured inside simple breast phantoms of different thicknesses. The experimental values were well consistent with the values calculated by Geant4. Then a detailed breast model with a CBT of 4 cm and a glandular content of 50%, which has been constructed in previous work, was used to study the effects of the distribution of glandular tissues in breast with Geant4. The breast model was reversed in direction of compression to get a reverse model with a different distribution of glandular tissues. Depth dose distributions and glandular tissue dose conversion coefficients were calculated. It revealed that the conversion coefficients were about 10% larger when the breast model was reversed, for glandular tissues in the reverse model are concentrated in the upper part of the model.
© The Authors, published by EDP Sciences, 2017
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