Poster ‐ 09: A MATLAB‐based Program for Automated Quality Assurance of a Prostate Brachytherapy Ultrasound System
Bibliographic record
Abstract
Purpose: A robust Quality Assurance (QA) program is essential for prostate brachytherapy ultrasound systems due to the importance of imaging accuracy during treatment and planning. Task Group 128 of the American Association of Physicists in Medicine has recommended a set of QA tests covering grayscale visibility, depth of penetration, axial and lateral resolution, distance measurement, area measurement, volume measurement, and template/electronic grid alignment. Making manual measurements on the ultrasound system can be slow and inaccurate, so a MATLAB program was developed for automation of the described tests. Methods: Test images were acquired using a BK Medical Flex Focus 400 ultrasound scanner and 8848 transducer with the CIRS Brachytherapy QA Phantom – Model 045A. For each test, the program automatically segments the inputted image(s), makes the appropriate measurements, and indicates if the test passed or failed. The program was tested by analyzing two sets of images, where the measurements from the first set were used as baseline values. Results: The program successfully analyzed the images for each test and determined if any action limits were exceeded. All tests passed – the measurements made by the program were consistent and met the requirements outlined by Task Group 128. Conclusions: The MATLAB program we have developed can be used for automated QA of an ultrasound system for prostate brachytherapy. The GUI provides a user‐friendly way to analyze images without the need for any manual measurement, potentially removing intra‐ and inter‐user variability for more consistent results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".