Radiation dose Tracking in Digital Mammography: Evaluation of Population Profiles Through Automatic Data Extraction from the DICOM Header

Authors

  • Dr Homero Schiab

Abstract

International regulatory organizations for quality control of X-ray systems, such as the International Atomic Energy Agency (IAEA), have implemented protocols for acceptance
testing of digital mammography and breast tomosyn thesis equipment, aiming to establish quality standards in radiology services. However, these guidelines are usually based on tests with breast phantoms with standardized thicknesses and compositions, often not representative of the patient population profiles at those services. In a prior study, we developed a computational system designed to automatically tracking and managing data extracted from the image acquisition processes of digital mammography and breast tomosyn thesis, stored on a DICOM SCP (Service Class Provider) server. This approach enables obtaining technical reports characterizing exposure parameters and tests have shown that the reference levels outlined in international standards for breast composition and radiation dose do not accurately reflect the characteristics of the actual patient population. Thus this study describes data collection and corresponding analysis for the dose tracking process primarily on three digital mammography systems of different radiological services. Extensive image datasets from these systems were obtained using a new application described previously, with a focus on dose profiles generated during exposures. Graphical representations resulting from the datasets are presented, along with analysis of skin entrance and mean glandular doses distributions, average kV and mAs applied during the exams together with the target/filter combinations, radiographic density distributions, as well as the age and breast thickness characteristic of the respective population submitted to exposures in each of those mammography services. Additionally, the extent of information and ease of acquisition provided by the tool for performance evaluation of digital mammography services is discussed.

References

(2021) World Health Organization CANCER. https://www.who.int/health-topics/cancer#tab=tab_1

K. Doi, M. L. Giger, R. M. Nishiskawa, R. A. Schmidt (1996) Digital mammography. 481.

(1993) Evaluation and routine testing in medical imaging departments - Part 1: General Aspects.

B. S. Monsees (2000) The mammography quality standards act: an overview of the regulations and guidance. https://doi.org/10.1016/50033-8389(05)70199-8

N. Perry, M. Broeders, C. D. Wolf, S Tomberg, R. Holland, L. Von Kersa (2008) European guidelines for quality assurance in breast cancer screening and diagnosis. http://www.euref.org/downloads?download=24:european-guidelines-for-quality-assurance-in-breast-cancer-screening-and-diagnosis-pdf

C. J. Kotre (2011) Statistical analysis of mammographic breast composition measurements: towards a quantitative measure of relative breast cancer risk. 84, 153-160.

(2016) Digital Imaging and Communications in Medicine (DICOM). Part 6: Data Dictionary.

Barufaldi B, Schiabel H, Maidment ADA (2019) Design and implementation of a radiation dose tracking and reporting system for mammography and digital breast tomosynthesis. 58, 131-140. https://doi.org/10.1016/j.ejmp.2019.02.011

Schiabel H., Barufaldi B., Ruberti Filha E. M. (2019) Investigations on a computer application for tracking the mean glandular breast dose profile in mammography. 76, 869-873. https://doi.org/10.1007/978-3-030-31635-8_104

Dance D.R., Young K.C., Van Engen R.E. (2009) Further factors for the estimation of mean glandular dose using the United Kingdom, European and IAEA breast dosimetry protocols. 54(14), 4361-4372.

B. M. Keller, D. L. Nathan, Y. Wang, G. Y. Zheng, J. C. Gee, E. F. Conant, D. Kontos (2012) Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation. 39(8), 4903-4917.

(2011) Quality Assurance Programme for Digital Mammography.

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Published

2023-11-15

How to Cite

Radiation dose Tracking in Digital Mammography: Evaluation of Population Profiles Through Automatic Data Extraction from the DICOM Header. (2023). London Journal of Medical and Health Research, 23(11), 1-25. https://journalspress.uk/index.php/LJMHR/article/view/769