Poster ‐ 10: QA of Ultrasound Images for Prostate Brachytherapy
Bibliographic record
Abstract
Purpose: The current QA protocol of ultrasound systems used in prostate brachytherapy (TG128) addresses geometrical verifications, but the scope of evaluation of image quality is limited. We recognized importance of the latter in routine practice, and designed a protocol for QA of the images. Methods: Images of an ultrasound prostate phantom (CIRS053) were collected with BK Flex Focus 400. The images were saved as bmp after adjusting the gain to 50% for consistent results. Mean pixel values and signal to noise ratio were inspected in the representative sections of the phantom, including the mock prostate and the unechoic medium. Constancy of these numbers over a one year period was looked at. Results: The typical intensity in the mock prostate region in the transverse images ranged between 95 and 118 (out of 256), and the signal to noise was about 10. The intensity in the urethra region was about 170±40, and the unechoic medium was 2±2. The mean and the signal to noise ratio remained almost unchanged after a year, while the signal in the unechoic medium increased to about 7±4. Similar values were obtained in the sagittal images. Conclusions: The image analysis discussed above allows quick evaluation of constancy of the image quality. This may be also useful in troubleshooting image‐quality problems during routine exams, which might not be due to deterioration of the US system, but other reasons, e.g. variations in tissue properties or air being trapped between the probe and the anatomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".