ArticleJournal of applied clinical medical physics2026
FilmQADose: Open-source software for radiochromic film dosimetry.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeFilmQADose is an open-source, Python-based software for radiochromic film dosimetry, designed to provide medical physicists with a freely available, extensible tool for two-dimensional dose verification in radiotherapy. It covers the full film QA workflow, from calibration curve generation to plan-to-film comparison, targeting clinical and research centers that require an accessible alternative to commercial solutions. DEVELOPMENT AND VALIDATION
methodsThe software was developed in Python using open-source scientific libraries for image processing, curve fitting, DICOM handling, and gamma analysis. Core functionalities include rational and polynomial calibration models, multichannel dose reconstruction, normalized cross-correlation template matching for automatic alignment, and gamma analysis. Validation was performed using irradiated Gafchromic EBT3 films in square field, pyramidal, and breast IMRT plans. Measured dose maps were compared against treatment planning system calculations using 3%/3 mm gamma criteria. DATA FORMAT AND USAGE NOTES: FilmQADose processes TIFF images acquired from flatbed scanners and DICOM RT Dose files exported from treatment planning systems. Output dose maps are generated in DICOM 2D format. The software is implemented in Python with standard scientific Python dependencies and includes a graphical user interface built using PySide6. The source code, documentation, and usage guides are publicly available at: eduardoh27.github.io/FilmQADose. POTENTIAL APPLICATIONS: The software supports clinical quality assurance in IMRT dose verification, as well as for research applications in radiochromic film dosimetry methodology. A limitation is the absence of lateral response artifact correction and recent machine-learning-based calibration methods.
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