Fractional flow reserve measurement using dynamic CT perfusion imaging in patients with coronary artery disease
Bibliographic record
Abstract
Abstract Purposes The objective was to evaluate the accuracy of a novel CT dynamic angiographic imaging (CT-DAI) algorithm for rapid fractional flow reserve (FFR) measurement in patients with coronary artery disease (CAD). Materials and Methods This retrospective study included 14 patients (age 58.5 ± 10.6 years, 11 males) with CAD who underwent stress dynamic CT myocardial perfusion scanning with a dual-source CT scanner. The included patients had analyzable proximal and distal coronary artery segments adjacent to the stenosis in the perfusion images and had corresponding invasive catheter-based FFR measurements for that stenosis. An in-house software based on the CT-DAI algorithm was used to compute FFR using the pre- and post- lesion coronary time-enhancement curves obtained from the stress myocardial perfusion images. The CT-DAI derived FFR values were then compared to the corresponding catheter-based invasive FFR values. A coronary artery stenosis was considered functionally significant for FFR value <0.8. Results The CT-DAI derived FFR values were in agreement with the invasive FFR values in all 15 coronary arteries in 14 patients, resulting in 100% per-vessel and per-patient diagnostic accuracy. FFR derived using CT-DAI (M = 0.768, SD = 0.156) showed an excellent linear correlation (R = 0.910, P < .001) and statistical indifference (P= .655) with that measured using invasive catheter-based method (M = 0.796, SD = 0.149). Bland-Altman analysis showed no significant proportional bias. Conclusion The novel CT-DAI algorithm can reliably compute FFR across a coronary artery stenosis directly from dynamic CT myocardial perfusion images, facilitating rapid on-site hemodynamic assessment of the epicardial coronary artery stenosis in patients with CAD.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".