Welcome to PyBDC Documentation!

PyBDC is a Python toolkit for calculating mean glandular dose for breast CT. The following software enables accurate breast dosage estimations based on four different models from well published papers. The software can be used on Windows, Mac, and Linux using a Python based GUI. The different methods along with the input parameters are explained in this documentation. The purpose of this tool is to allow breast CT researchers, medical physicists, and developers a simple method for comparing the dosage between different CT systems to ensure substantial equivalence.

Table of Contents

Methods Overview

Currently supports the following breast models:

  • Sarno Any Spectrum

  • Hernandez Any Spectrum

  • Sarno 49 kVp W Spectra

  • Sechopoulus 49 kvP W Spectra

Detailed descriptions on individual methods are provided in section Methods Overview.

PyBDC Team:

Harsha Marupudi, Jospeh Manus, Bahaa Ghammraoui

License

CC0 1.0 Universal

Statement of Purpose

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Disclaimer

This software and documentation was developed at the Food and Drug Administration (FDA) by employees of the Federal Government in the course of their official duties. Pursuant to Title 17, Section 105 of the United States Code, this work is not subject to copyright protection and is in the public domain. Permission is hereby granted, free of charge, to any person obtaining a copy of the Software, to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, or sell copies of the Software or derivatives, and to permit persons to whom the Software is furnished to do so. FDA assumes no responsibility whatsoever for use by other parties of the Software, its source code, documentation or compiled executables, and makes no guarantees, expressed or implied, about its quality, reliability, or any other characteristic. Further, use of this code in no way implies endorsement by the FDA or confers any advantage in regulatory decisions. Although this software can be redistributed and/or modified freely, we ask that any derivative works bear some notice that they are derived from it, and any modified versions bear some notice that they have been modified.

Citations:

Sarno, A., Mettivier, G., Di Lillo, F., & Russo, P. (2017). A Monte Carlo study of monoenergetic and polyenergetic normalized glandular dose (DgN) coefficients in mammography. Physics in medicine and biology, 62(1), 306–325. https://doi.org/10.1088/1361-6560/62/1/306

Hernandez, A. M., Becker, A. E., & Boone, J. M. (2019). Updated breast CT dose coefficients (DgNCT ) using patient-derived breast shapes and heterogeneous fibroglandular distributions. Medical physics, 46(3), 1455–1466. https://doi.org/10.1002/mp.13391

Sechopoulos, I., Feng, S. S., & D’Orsi, C. J. (2010). Dosimetric characterization of a dedicated breast computed tomography clinical prototype. Medical physics, 37(8), 4110–4120. https://doi.org/10.1118/1.3457331