Development of a Novel Breast MRI Phantom for Quality Control
Bibliographic record
Abstract
OBJECTIVE: Many indications for breast MRI exist. MRI screening can identify preinvasive breast cancer in women at high risk and in that regard is superior to mammography and ultrasound. Quality control standards exist for mammography and breast ultra-sound screening with phantoms designed specifically for this purpose. Given the growing importance of breast MRI, we propose the development of a breast MRI phantom for quality control purposes. MATERIALS AND METHODS: A breast phantom with dual cavities containing water and fat was developed. A resolution plate inside the phantom contains various shapes ranging in size from 1 to 20 mm. Twenty studies of the phantom were performed with a 1.5-T system. STIR, T1-weighted fat-suppressed, and T2-weighted sequences were completed. Relaxation times of water and fat, number of step shapes resolved on STIR and T2-weighted images, number of circles resolved on T2-weighted images, and the diameter of a 20-mm circle on T1-weighted fat-suppressed images were recorded. RESULTS: On STIR images the TR of fat was 238.70±96.31 ms and of water was 1231.92±399.14 ms. On T2-weighted images the TR of fat was 778.73±62.60 ms and of water was 1737.60±121.63 ms. On STIR images, steps 3 mm and larger were visualized in 95% of instances. On T2-weighted images steps 3 mm and larger were seen in all instances. Measurements of a 20-mm circle were 19±0.3 mm. CONCLUSION: The proposed breast MRI phantom can be used to obtain reproducible measurements and allows implementation of quality control measures for a modality that is being increasingly used.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".