Quantitative low-dose rest and stress CT myocardial perfusion imaging with a whole-heart coverage scanner improves functional assessment of coronary artery disease
Bibliographic record
Abstract
OBJECTIVE: We evaluated the diagnostic accuracy of myocardial blood flow (MBF) and perfusion reserve (MPR) measured from low-dose dynamic contrast-enhanced (DCE) imaging with a whole-heart coverage CT scanner for detecting functionally significant coronary artery disease (CAD). METHODS: Twenty one patients with suspected or known CAD had rest and dipyridamole stress MBF measurements with CT and SPECT myocardial perfusion imaging (MPI), and lumen narrowing assessment with coronary angiography (catheter and/or CT based) within 6 weeks. SPECT MBF measurements and coronary angiography were used together as reference to determine the functional significance of coronary artery stenosis. In each CT MPI study, DCE images of the whole heart were acquired with breath-hold using a low-dose acquisition protocol to generate MBF maps. Binomial logistic regression analysis was used to determine the diagnostic accuracy of CT-measured MBF and MPR (ratio of stress to rest MBF) for assessing functionally significant coronary stenosis. RESULTS: Mean stress MBF and MPR in ischemic segments were lower than those in non-ischemic segments (1.37 ± 0.34 vs. 2.14 ± 0.64 ml/min/g; 1.56 ± 0.41 vs. 2.53 ± 0.70; p < 0.05 for all). The receiver operating characteristic curve analysis revealed that MPR (AUC 0.916, 95%CI: 0.885-0.947) had a superior power than stress MBF (AUC 0.869, 95%CI: 0.830-0.909) for differentiating non-ischemic and ischemic myocardial segments (p = 0.045). On a per-vessel and per-segment analysis, concomitant use of MPR and stress MBF thresholds further improved the diagnostic accuracy compared to MPR or stress MBF alone for detecting obstructive coronary lesions (per-vessel: 93.4% vs. 83.6% and 88.5%, respectively; per-segment: 90.0% vs. 83.7% and 83.1%, respectively). The estimated effective dose of a rest and stress CT MPI study was 3.04 and 3.19 mSv respectively. CONCLUSION: Quantitative rest and stress myocardial perfusion measurement with a large-coverage CT scanner improves the diagnostic accuracy for detecting functionally significant coronary stenosis.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".