THUBreast: an open-source breast phantom generation software for x-ray imaging and dosimetry

Abstract

Objective. Establishing realistic phantoms of human anatomy is a continuing concern within virtual clinical trials of breast x-ray imaging. However, little attention has been paid to glandular distribution within these phantoms. The principal objective of this study was to develop breast phantoms considering the clinical glandular distribution. Approach. This research introduces an innovative method for integrating glandular distribution information into breast phantoms. We have developed an open-source software, THUBreast 4 4 http://github.com/true02Hydrogen/THUBreast/ , which generates breast phantoms that accurately replicate both the structural texture and glandular distribution, two crucial elements in breast x-ray imaging and dosimetry. To validate the efficacy of THUBreast, we assembled three groups of breast phantoms (THUBreast, patient-based, homogeneous) for irradiation simulation and calculated the power-law exponents ( β ) and mean glandular dose ( Dg ), indicators of texture realism and radiation risk, respectively, utilizing MC-GPU. Main results. Upon the computation of the Dg for the THUBreast phantoms, it was found to be in agreement with that absorbed by the phantoms based on patients, with an average deviation of 4%. The estimates of average Dg thus obtained were on average 23% less than those computed for the homogeneous phantoms. It was observed that the homogeneous phantoms did overestimate the average Dg by 30% when compared to the phantoms based on patients. The mean value of β for the images of THUBreast phantoms was found to be 2.92 ± 0.08, which shows a commendable agreement with the findings of prior investigations. Significance. It is evidently clear from the results that THUBreast phantoms have a preliminary good performance in both imaging and dosimetry in terms of indicators of texture realism and glandular dose. THUBreast represents a further step towards developing a powerful toolkit for comprehensive evaluation of image quality and radiation risk.