Reduced dose measurement of absolute myocardial blood flow using dynamic SPECT imaging in a porcine model
Bibliographic record
Abstract
PURPOSE: Absolute myocardial blood flow (MBF) and myocardial flow reserve (MFR) measurements provide important additional information over traditional relative perfusion imaging. Recent advances in camera technology have made this possible with single-photon emission tomography (SPECT). Low dose protocols are desirable to reduce the patient radiation risk; however, increased noise may reduce the accuracy of MBF measurements. The authors studied the effect of reducing dose on the accuracy of dynamic SPECT MBF measurements. METHODS: Nineteen 30-40 kg pigs were injected with 370 + 1110 MBq of Tc-99m sestamibi or tetrofosmin or 37 + 111 MBq of Tl-201 at rest + stress. Microspheres were injected simultaneously to measure MBF. The pigs were imaged in list-mode for 11 min starting at the time of injection using a Discovery NM 530c camera (GE Healthcare). Each list file was modified so that 3/4, 1/2, 1/4, 1/8, 1/16, and 1/32 of the original counts were included in the projections. Modified projections were reconstructed with CT-based attenuation correction and an energy window-based scatter correction and analyzed with FlowQuant kinetic modeling software using a 1-compartment model. A modified Renkin-Crone extraction function was used to convert the tracer uptake rate K1 to MBF values. The SPECT results were compared to those from microspheres. RESULTS: Correlation between SPECT and microsphere MBF values for the full injected activity was r ≥ 0.75 for all 3 tracers and did not significantly degrade over all count levels. The mean MBF and MFR and the standard errors in the estimates were not significantly worse than the full-count data at 1/4-counts (Tc99m-tracers) and 1/2-counts (Tl-201). CONCLUSIONS: Dynamic SPECT measurement of MBF and MFR in pigs can be performed with 1/4 (Tc99m-tracers) or 1/2 (Tl-201) of the standard injected activity without significantly reducing accuracy and precision.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".