Measurement of Myocardial Blood Flow by SPECT
Bibliographic record
Abstract
Abstract Stress MPI with SPECT and PET is widely used for diagnosis and determining prognosis in patients with suspected or known CAD. However, a major limitation of MPI is the use of relative perfusion for diagnosis of obstructive CAD and leads to underestimation of the extent of obstructive CAD. However, MBF and MFR can be measured with PET and provide additional diagnostic and prognostic value over relative PET MPI. Similarly, SPECT measurement of MBF may improve the clinical value of SPECT and greatly increase the availability and use of MBF with MPI. The accuracy of CZT SPECT measurement of MBF has been validated in clinical evaluations in patients undergoing coronary angiography and PET imaging. Reduced global stress MBF and MFR can identify the presence of severe MVD. Regional reductions in stress MBF and MFR have high diagnostic accuracy for specific vessel CAD. Reduced global and regional SPECT MFR can predict reduced global and regional PET MFR. SPECT measurement of MBF has good day-to-day repeatability and interobserver variability. We describe the use of SPECT MBF at the University of Ottawa Heart Institute, including image acquisition and processing and clinical interpretation of test results with case examples. The clinical use of SPECT MBF is in the early stages of implementation. Protocols including radiotracers, camera systems and software need to be standardized. Multicenter studies are necessary to better define the diagnostic value of CZT SPECT MBF for obstructive CAD and the incremental prognostic value of SPECT MBF measurement compared to relative MPI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".