The effect of SPECT reconstruction corrections on the absolute and relative quantitative accuracy of myocardial perfusion studies
Bibliographic record
Abstract
This paper reports the findings of investigations into the performance of SPECT corrections for photon attenuation, distance-dependent resolution loss and photon scatter on the absolute and relative quantitative accuracy of myocardial perfusion studies. The measurements of myocardial wall thickness and myocardial infarct size were used to estimate the accuracy of the absolute and relative quantitative accuracy, respectively. A series of phantom studies were performed and additional information was gathered from a group of 37 normal patients. Each set of data was reconstructed with (1) filtered-back projection (FBP), (2) ordered subset expectation maximization (OSEM), (3) OSEM plus attenuation correction (AC), (4) OSEM plus detector response compensation (DRC), (5) OSEM plus AC and DRC, and (6) OSEM plus AC, DRC and scatter correction (SC). The image analysis toolbox iQuant was used to perform the analysis. Both patient and phantom data showed SPECT image corrections to have a significant effect on myocardial wall thickness, with reconstructions involving SC providing the most accurate results. Phantom data showed that good estimates of anterior and lateral wall infarct sizes are provided by all reconstruction techniques, whereas good estimates of inferior and septal wall infarct sizes are only provided by reconstructions including AC. Accurate measurement of in fa rets in any location and of any size is only possible with SC. This analysis suggests that in order to achieve perfect quantitative accuracy of myocardial perfusion studies, corrections for photon attenuation, distance-dependent resolution loss and photon scatter should be applied. However, for routine clinical analysis involving visual interpretation and an estimate of infarct size, the application of attenuation correction and detector resolution compensation might be considered sufficient.
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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 | 0.013 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".