A constancy test to monitor cross-calibration factors for a small animal SPECT-CT scanner
Bibliographic record
Abstract
2014 Objectives To determine if a sealed reference source for a dose calibrator (DC) can be used to cross-calibrate a small animal SPECT scanner with the DC to enable monitoring of the constancy of scanner performance. Methods Using a nanoSPECT-CT scanner, 15 scans of a sealed Co-57 vial source were obtained over a period of one month. All scans were acquired and reconstructed with the same parameters. Quantification factors (QF) for cross-calibration were derived for each scan relative to the DC measurement of the reference Co-57. Additionally, 8 other QF were derived from 7 previous months. A comparison of QF prior to and after routine preventative service (uniformity calibration) was performed. Mean (M), standard deviation (SD) and coefficient of variation (CV) for QF were calculated. Results The range was 4.06-4.198 with the M ± SD of 4.151 ± 0.048 for the 15 QF over one month (all values x 10-3). The CV was 1.2%. Prior to service being performed on 4 of the 8 additional QF the range was 4.412-4.683, the M ± SD was 4.544 ± 0.116, and CV was 2.6%. We were able to detect changes in the value and variation of QF using the sealed source. Conclusions In this study cross-calibration of the SPECT scanner to DC was performed using a Co-57 reference source to derive QF, these QF allowed monitoring of the constancy of scanner performance. Service to the scanner can change the QF used for cross-calibration, so QF should be re-calculated after both routine service as well as repair to ensure the scanner is properly calibrated to the DC.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".