Test-Retest Precision of Myocardial Blood Flow Measurements With <sup>99m</sup> Tc-Tetrofosmin and Solid-State Detector Single Photon Emission Computed Tomography
Bibliographic record
Abstract
Background: Measurement of myocardial blood flow (MBF) with single photon emission computed tomography (SPECT) is feasible using cardiac cameras with solid-state detectors. SPECT MBF has been shown to be accurate when compared with positron emission tomography MBF measured in the same patients. However, the value of a test result applied to an individual patient depends strongly on the precision or repeatability of the test. The purpose of our study is to measure the precision of SPECT MBF measurements using 99m Tc-tetrofosmin and a solid-state cardiac camera. Methods: SPECT MBF was measured in 30 patients and repeated at a mean interval of 18 days. MBF was evaluated from images with and without attenuation correction based on a separately acquired CT scan. The dynamic images were processed independently by 2 operators using in-house kinetic analysis software that applied a 1-tissue-compartment model. The K1 rate constant was converted to MBF using previously determined extraction fraction corrections. Correction for patient body motion was applied manually. Results: The average coefficient of variation (COV) in the differences between the 2 MBF measurements was between 28% and 31%. The interobserver COV was between 11% and 15%. Myocardial flow reserve is the ratio of MBF measured at stress and rest, and the COV is correspondingly higher. The COV for the difference in repeated myocardial flow reserve was 33% to 38%, whereas the interobserver COV was 13% to 22%. Conclusions: The COV for the difference in SPECT MBF measurements obtained on separate days is 28% to 31%. The corresponding COV for myocardial flow reserve is 33% to 38%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".