Dynamic SPECT Measurement of Absolute Myocardial Blood Flow in a Porcine Model
Bibliographic record
Abstract
UNLABELLED: Absolute myocardial blood flow (MBF) and myocardial flow reserve (MFR) provide incremental diagnostic and prognostic information over relative perfusion alone. Recent development of dedicated cardiac SPECT cameras with better sensitivity and temporal resolution make dynamic SPECT imaging more practical. In this study, we evaluate the measurement of MBF using a multipinhole dedicated cardiac SPECT camera in a pig model of rest and transient occlusion at stress using 3 common tracers: (201)Tl, (99m)Tc-tetrofosmin, and (99m)Tc-sestamibi. METHODS: Animals (n = 19) were injected at rest/stress with (99m)Tc radiotracers (370/1,100 MBq) or (201)Tl (37/110 MBq) with a 1-h delay between rest and dipyridamole stress. With each tracer, microspheres were injected simultaneously as the gold standard measurement for MBF. Dynamic images were obtained for 11 min starting with each injection. Residual resting activity was subtracted from stress data and images reconstructed with CT-based attenuation correction and energy window-based scatter correction. Dynamic images were processed with kinetic analysis software using a 1-tissue-compartment model to obtain the uptake rate constant K(1) as a function of microsphere MBF. RESULTS: Measured extraction fractions agree with those obtained previously using ex vivo techniques. Converting K(1) back to MBF using the measured extraction fractions produced accurate values and good correlations with microsphere MBF: r = 0.75-0.90 (P < 0.01 for all). The correlation in the MFR was between r = 0.57 and 0.94 (P < 0.01). CONCLUSION: Noninvasive measurement of absolute MBF with stationary dedicated cardiac SPECT is feasible using common perfusion 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".