The effect of patient, acquisition and reconstruction variables on myocardial wall thickness as measured from myocardial perfusion SPECT studies
Bibliographic record
Abstract
In assessing the size and severity of myocardial perfusion defects, either a count threshold is applied to the images, or they are compared to a database of healthy hearts. This study aims to determine the dependence of these databases and thresholds on patient, acquisition and reconstruction variables, by measuring myocardial wall thickness. Analysis was performed on myocardial perfusion studies from 38 normal patients and a series of phantom experiments. The variables investigated included patient gender, test type, liver interference, myocardium to background activity ratio, acquisition zoom factor, matrix size and reconstruction type. When attenuation correction (AC) and detector resolution compensation (DRC) was applied during reconstruction, no significant difference was found in myocardial wall thickness between males and females, rest and stress studies, the presence and absence of liver interference, or clinically relevant myocardium to background activity ratios. A significant difference was found between standard and zoomed acquisitions, and between simple reconstruction techniques and those containing SPECT corrections. Results suggest that when AC and DRC are applied during reconstruction, patient variables do not influence quantitative accuracy and therefore analysis does not require individual databases or thresholds. As reconstruction methods improve in accuracy and in their ability to reconstruct large matrices, new databases and thresholds will be needed, bringing us closer to perfect absolute quantitative accuracy
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".