Accuracy of mammography dosimetry in the era of the European Directive 2013/59/Euratom transposition

Radiation Dose of Contrast-Enhanced Mammography: A Two-Center Prospective Comparison

The radiation dose associated with contrast-enhanced mammography (CEM) has been investigated only by single-center studies. In this retrospective study, we aimed to compare the radiation dose between two centers performing CEM within two prospective studies, using the same type of equipment. The CEM mean glandular dose (MGD) was computed for low energy (LE) and high energy (HE) images and their sum was calculated for each view. MGD and related parameters (entrance dose, breast thickness, compression, and density) were compared between the two centers using the Mann−Whitney test. Finally, per-patient MGD was calculated by pooling the two datasets and determining the contribution of LE and HE images. A total of 348 CEM examinations were analyzed (228 from Center 1 and 120 from Center 2). The median total MGD per view was 2.33 mGy (interquartile range 2.19−2.51 mGy) at Center 1 and 2.46 mGy (interquartile range 2.32−2.70 mGy) at Center 2, with a 0.15 mGy median difference (p < 0.001) equal to 6.2%. LE-images contributed between 64% and 77% to the total patient dose in CEM, with the remaining 23−36% being associated with HE images. The mean radiation dose for a two-view bilateral CEM exam was 4.90 mGy, about 30% higher than for digital mammography.

Diagnostic reference levels in digital mammography: a systematic review

Diagnostic reference levels (DRLs) in digital mammography (DM) serve as a useful benchmark for dose monitoring and optimisation, allowing comparison amongst countries, institutions and mammography units. A systematic review of DRLs in DM, published in 2014, reported a lack of consistent and internationally accepted protocol in DRLs establishment, thereby resulting in wide variations in methodologies which complicates comparability between studies. In 2017, the International Commission of Radiation Protection (ICRP) published additional guidelines and recommendations to provide clarity in the protocol used in DRLs establishment. With the continuing evolvement of technology, optimisation of examinations and updates in guidelines and recommendations, DRLs should be revised at regular intervals. This systematic review aims to provide an update and identify a more consistent protocol in the methodologies used to establish DRLs. Searches were conducted through Web of Science, PubMed-MEDLINE, ScienceDirect, CINAHL and Google Scholar, which resulted in 766 articles, of which 19 articles were included after screening. Relevant data from the included studies were summarised and analysed. While the additional guidelines and recommendations have provided clarifications in the methodologies used in DRLs establishment, such as data source (i.e. the preference to use data derived from patient instead of phantoms to establish DRLs), protocol (i.e. stratification of DRLs by compressed breast thickness and detector technology, and the use of median value for DRLs quantity instead of mean) and percentiles used to establish DRLs (i.e. set at the 75th percentile with a minimum sample size of 50 patients), other differences such as the lack of a standard dose calculation method used to estimate mean glandular dose continues to complicate comparisons between studies and different DM systems. This systematic review update incorporated the updated guidelines and recommendations from ICRP which will serve as a useful resource for future research efforts related to DRLs, dose monitoring and optimisation.

Breast glandularity and mean glandular dose assessment using a deep learning framework: Virtual patients study

PURPOSE

Breast dosimetry in mammography is an important aspect of radioprotection since women are exposed periodically to ionizing radiation due to breast cancer screening programs. Mean glandular dose (MGD) is the standard quantity employed for the establishment of dose reference levels in retrospective population studies. However, MGD calculations requires breast glandularity estimation. This work proposes a deep learning framework for volume glandular fraction (VGF) estimations based on mammography images, which in turn are converted to glandularity values for MGD calculations.

METHODS

208 virtual breast phantoms were generated and compressed computationally. The mammography images were obtained with Monte Carlo simulations (MC-GPU code) and a ray-tracing algorithm was employed for labeling the training data. The architectures of the neural networks are based on the XNet and multilayer perceptron, adapted for each task. The network predictions were compared with the ground truth using the coefficient of determination (r²).

RESULTS

The results have shown a good agreement for inner breast segmentation (r² = 0.999), breast volume prediction (r² = 0.982) and VGF prediction (r² = 0.935). Moreover, the DgN coefficients using the predicted VGF for the virtual population differ on average 1.3% from the ground truth values. Afterwards with the obtained DgN coefficients, the MGD values were estimated from exposure factors extracted from the DICOM header of a clinical cohort, with median(75 percentile) values of 1.91(2.45) mGy.

CONCLUSION

We successfully implemented a deep learning framework for VGF and MGD calculations for virtual breast phantoms.