Sci—Thur PM: Imaging — 04: An iterative triple energy window (TEW) approach to cross talk correction in quantitative small animal Tc99m and In111 SPECT
Bibliographic record
Abstract
Dual isotope SPECT allows simultaneous measurement of two different tracers in vivo. With In111 (emission energies of 171keV and 245keV) and Tc99m (140keV), quantification of Tc99m is degraded by cross talk from the In111 photons that scatter and are detected at an energy corresponding to Tc99m. TEW uses counts recorded in two narrow windows surrounding the Tc99m primary window to estimate scatter. Iterative TEW corrects for the bias introduced into the TEW estimate resulting from un‐scattered counts detected in the scatter windows. The contamination in the scatter windows is iteratively estimated and subtracted as a fraction of the scatter‐corrected primary window counts. The iterative TEW approach was validated with a small‐animal SPECT/CT camera using a 2.5mL plastic container holding thoroughly mixed Tc99m/In111 activity fractions of 0.15, 0.28, 0.52, 0.99, 2.47 and 6.90. Dose calibrator measurements were the gold standard. Uncorrected for scatter, the Tc99m activity was over‐estimated by as much as 80%. Unmodified TEW underestimated the Tc99m activity by 13%. With iterative TEW corrections applied in projection space, the Tc99m activity was estimated within 5% of truth across all activity fractions above 0.15. This is an improvement over the non‐iterative TEW, which could not sufficiently correct for scatter in the 0.15 and 0.28 phantoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.019 | 0.010 |
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".