Assessment of myocardial perfusion reserve with blood oxygen level-dependent cardiovascular magnetic resonance imaging
Bibliographic record
Abstract
New Blood Oxygen Level-Dependent Cardiovascular Magnetic Resonance Imaging (BOLD-CMR) sequences show a high sensitivity and consistent image quality that allows for assessing tissue oxygenation. We hypothesized that BOLD-CMR can quantitatively assess myocardial blood flow changes using myocardial oxygenation as a biomarker. To test whether a BOLD-CMR sequence accurately estimates myocardial perfusion changes. Six anesthetized mongrel dogs were instrumented with a coronary infusion catheter in the circumflex coronary artery (LCX), an MR-compatible epivascular flow probe around the LCX and a catheter in the coronary sinus. Using a clinical 1.5 T MRI system (MAGNETOM Avanto, Siemens Healthcare, Germany), SSFP BOLD-CMR was performed during graded intracoronary infusion of adenosine in the LCX. Typical scan parameters were: Field-of view (FOV) 190 × 280 mm; matrix size 106 × 192; slice thickness 10 mm; T R /T E 5.8/2.9 ms; flip angle 90°; typical breath-hold duration 14 s. Images were analyzed using clinically validated software (cmr , Circle Cardiovascular Imaging Inc., Calgary, Canada) and the BOLD signal intensity (SI) for each was calculated. Correlations of coronary flow, oxygen saturation in the coronary sinus and myocardial BOLD-CMR signal intensity (BOLD-SI) changes were calculated by regression analysis. The same CMR imaging protocol was used in 11 healthy volunteers (6 male, 5 female) before, during and after intravenous adenosine infusion (140 micro-g/kg). Myocardial perfusion reserve in the human volunteers was calculated from flow measurement in the coronary sinus using velocity-encoded CMR. In dogs, adenosine-induced blood flow changes in the LCX agreed very well with changes in coronary venous saturation (logarithmic scale, r = 0.94, p < 0.001). Furthermore, coronary venous saturation showed a strong yet linear correlation with BOLD-SI changes (r = 0.80, p < 0.001). Consequently, as shown in Figure 1 , blood flow changes correlated very well with the BOLD-SI (r = 0.84, p < 0.001). The exponential correlation is described by the equation (y) = 98.3+25.4*(1-e ) (x = flow, y = BOLD-SI). In the volunteers, adenosine infusion resulted in a significant myocardial perfusion increase (416 ± 69% of baseline, p < 0.001). BOLD SI increased significantly by 20.1 ± 9.5% (p < 0.001 as compared to baseline). The reproducibility of the BOLD-SI in the two baseline measurements before and after adenosine infusion was excellent (mean difference 0.1 ± 2.6%, p = 0.97). Blood flow changes and BOLD-SI in canine model under adenosis infusion . State-of-the-art BOLD-sensitive MRI sequences detect changes of myocardial perfusion in an experimental animal model and in humans in vivo. This technique may allow for an accurate, non-invasive assessment of myocardial perfusion reserve in humans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".