SU‐GG‐T‐329: Gafgui: An Open‐Source Project for Radiochromic Film Dosimetry
Bibliographic record
Abstract
Purpose: A code, named Gafgui, is developed to handle most features required for efficient and accurate radiochromic film dosimetry. Gafgui is distributed using the GNU General Public Licence in order to allow the medical physics community to have access to a performing analysis tool and to share algorithms. This could contribute to improving the efficiency of research projects and clinical applications, especially for centers where the access to such tool is limited. Materials: The code is developed using the Matlab® (The Mathworks, Inc., Boston, MA) platform and is designed for both clinical and research use. Gafgui handles 48‐bits tiff images format as well as RIT (Radiological Imaging Technology, CO) images. Single or dual‐channel analysis modes are available, to allow analysis of, for example, EBT and EBT2 films (International Specialty Products, Wayne, NJ). A variety of tools are included: image manipulation, an automated film characterization method, an automated method for scanner homogeneity correction as well as a complete uncertainty analysis of film response based on a recent exhaustive study of realistic uncertainties in EBT film dosimetry, suitable for clinical or research applications. Data analysis include average dose over a region of interest as well 1D and 2D‐distributions. Results: In our center, the development of Gafgui has allowed improving the workflow in radiochromic film dosimetry. It has also enabled the implementation of IMRT dose verifications as well as research in nonstandard beams reference dosimetry. The code design shows great potential for developing applications, since users are allowed to modify or add features as they wish and share it with other users. Future developments will be accessible in regularly updated versions and available to the medical physics community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.101 | 0.078 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".