Quantification in positron emission mammography (PEM) with planar detectors: contrast resolution measurements using a custom breast phantom and novel spherical hot-spots
Bibliographic record
Abstract
The authors have previously demonstrated that their Positron Emission Mammography-1 (PEM-1) system can successfully defect small (<2 cm diameter) tumors. Images in the current system are judged qualitatively for the presence of focal uptake of /sup 18/F-FDG. The authors are presently taking steps toward quantifying the emission images. To this end, they have constructed a breast phantom and developed a novel technique for fabricating very small wall-less radioactive hot-spots. The hot-spots are made by adding /sup 18/F-FDG to a 25 mg/cc solution of Agarose in water. The heated solution is poured into spherical molds which are separated upon congealing to yield robust wall-less radioactive hot-spots. The hot-spots were found to be uniform to within 1-5 parts in 100. Less than 0.1% of the total hot-spot activity leaked into the background in 30 minutes. Contrast resolution experiments have been performed with 12 mm and 16 mm diameter hot-spots in the breast phantom containing water with various amounts of background activity. In both cases, the observed contrast values agree well with the ideal values. In the case of the 12 mm hot-spot with a 350-650 keV energy window, mean image contrast was 86/spl plusmn/4% of ideal contrast. The image contrast for 12 mm hot-spot improved by 40% and the number of detected events decreases by 35% when the low energy threshold was increased from 300 keV to 450 keV. Activity uptake in the heart was found to have negligible effect on the contrast resolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".