Bibliographic record
Abstract
This work develops methods to quantify absolute activity measurements with a multi-head pinhole cardiac SPECT camera that uses cadmium-zinc-telluride (CZT) detectors (Discovery NM530c, GE Healthcare).A central component of absolute activity quantification is the correction for Compton scattered photons.A modified Dual Energy Window (DEW) scatter correction (SC) method was developed that compensates for the presence of unscattered photons in the lower-energy window used to measure scatter.The DEW-SC method was validated using phantom experiments.The mean error in absolute activity measurement was 5 ± 4 % when correcting for attenuation, scatter and the partial volume effects.Clinical accuracy was assessed using 99m Tc-tetrofosmin myocardial perfusion images obtained on the NM530c and compared to DEW scatter-corrected images acquired on a conventional SPECT camera for the same patient.DEW-SC images acquired on the NM530c had increased noise but had summed perfusion scores that were not significantly different from those acquired on the conventional SPECT camera.To reduce noise in scatter corrected images and provide a more accurate estimate of scatter, a model-based SC method was developed based on the analytical photon distribution (APD) approach.Using physical phantom experiments and a set of five clinical studies, the accuracy of APD-SC was evaluated and compared to DEW-SC.Images generated using the model-based method agreed well with acquired data.APD-SC images had lower noise than DEW-SC images and provided a more accurate measure of cardiac activity in high-scatter scenarios.The developed methods List of AppendicesAppendix A -The 17 segment model……….….………………………………………………….
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".