Functional significance of Blood Oxygen Level Dependent (BOLD) imaging in patients with coronary artery disease - a validation study using fractional flow reserve
Bibliographic record
Abstract
Oxygen-sensitive BOLD CMR scans were performed in patients who were scheduled for clinically-indicated coronary angiography. BOLD images were captured during rest and adenosine-induced coronary hyperemia. The mean BOLD signal intensity (SI) percent changes were calculated between rest and hyperemia in the subendocardial myocardium at basal, mid, and apical regions. Segmental ΔSI% in the corresponding coronary territory was defined as ischemic (using a cut-off of <0.80) or non-ischemic by FFR. The BOLD segment with the lowest ΔSI% in the territory subtended by the FFR measurements was selected for statistical analysis. Bland-Altman analysis was used to assess the level of agreement between all segments analyzed by two blinded readers. Twenty-eight patients totaling 147 myocardial segments were available for analysis. 73 segments were excluded, with 66% of these being apical. The remaining 74 segments equated to 22 patients (60 +/- 9y, 19 males), eight of these had a normal FFR (≥ 0.80) and 14 had FFR values <0.80. Mean BOLD SI percent change was significantly less in patients with abnormal FFR values (-4.62 +/- 2.28% SEM), in comparison to patients with normal FFR values (8.54 +/- 3.08 % SEM); p=0.003. (See Figure 1 and 2 ). The Bland-Altman analysis indicated that the 95% limits of agreement between the two readers ranged from -23.6% to 27.8% (Figure 3 ). Comparison of BOLD Signal Intensity Percent Changes to FFR Pressure Wire Measurements Comparison of FFR Pressure Wire Measurements as a Continuous Variable to BOLD Signal Intensity Percent Changes Bland-Altman analysis for inter-observer variability Our pilot data indicate that BOLD-sensitive CMR may allow for selectively identifying hemodynamically relevant coronary artery stenoses based on a blunted hyperemic response to adenosine. Image quality (particularly in apical segments) remains a significant limitation of the BOLD technique, with this reflected in suboptimal interobserver variability. Most excluded segments were from early studies, suggesting improved acquisition quality with experience. Further recruitment will enable validation of sensitivity and specificity with identification of a standard threshold for ischemia identification.
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 | 0.002 | 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 | 0.001 | 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".