Sci—Fri AM: Imaging — 09: Serial estimation of cross‐talk for correction in dual‐isotope imaging with dynamic tracers
Bibliographic record
Abstract
The recent radioisotope shortage has led to interest in non-Tc99m-based tracers. We have developed a novel I-123-labelled myocardial perfusion imaging tracer. We compare the I123-tracer to the clinical standard of Tc99m tetrofosmin in vivo in a rat model using a small-animal SPECT/CT camera. SPECT distinguishes different isotopes based on the different energies of the emitted gamma rays and thus allows simultaneous comparison of two tracer distributions in the same animal. Dual-isotope imaging is complicated by cross-talk between the energy windows of the isotopes. Standard energy-window-based correction methods are difficult to employ because of the proximity in energy of Tc99m (140keV) and I123 (159keV). Imaging the second tracer's energy window prior to its injection provides an estimate of the cross-talk. However, this estimate is only accurate if the tracer distribution is static. We use serial imaging prior to the introduction of the second tracer to estimate the dynamics of the first tracer and interpolate the cross-talk images to provide a more accurate correction. We used rat models of myocardial disease (n=3). I123 tracer was injected and imaged for one hour at 20min intervals. The Tc99m tetrofosmin was then injected and 30min later, a dual-isotope image was obtained. The impact of this approach is assessed by comparing the differences in the Tc99m-tetrofosmin image using this method with correction by simple correction for physical decay. The interpolative approach improves the accuracy of the correction by 2%-5% and thereby enhances the comparison of the two tracers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.015 |
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".