Myocardial blood flow is the dominant factor influencing cardiac magnetic resonance adenosine stress T2
Bibliographic record
Abstract
Stress imaging identifies ischemic myocardium by comparing hemodynamics during rest and hyperemic stress. Hyperemia affects multiple hemodynamic parameters in myocardium, including myocardial blood flow (MBF), myocardial blood volume (MBV), and venous blood oxygen levels (PvO 2 ). Cardiac T2 is sensitive to these changes and therefore is a promising non‐contrast option for stress imaging; however, the impact of individual hemodynamic factors on T2 is poorly understood, making the connection from altered T2 to changes within the tissue difficult. To better understand this interplay, we performed T2 mapping and measured various hemodynamic factors independently in healthy pigs at multiple levels of hyperemic stress, induced by different doses of adenosine (0.14‐0.56 mg/kg/min). T1 mapping quantified changes in MBV. MBF was assessed with microspheres, and oxygen consumption was determined by the rate pressure product (RPP). Simulations were also run to better characterize individual contributions to T2. Myocardial T2, MBF, oxygen consumption, and MBV all changed to varying extents between each level of adenosine stress (T2 = 37.6‐41.8 ms; MBF = 0.48‐1.32 mL/min/g; RPP = 6507‐4001 bmp*mmHg; maximum percent change in MBV = 1.31%). Multivariable analyses revealed MBF as the dominant influence on T2 during hyperemia (significant β ‐values >7). Myocardial oxygen consumption had almost no effect on T2 ( β ‐values <0.002); since PvO 2 is influenced by both oxygen consumption and MBF, PvO 2 changes detected by T2 during adenosine stress can be attributed to MBF. Simulations varying PvO 2 and MBV confirmed that PvO 2 had the strongest influence on T2, but MBV became important at high PvO 2 . Together, these data suggest a model where, during adenosine stress, myocardial T2 responds predominantly to changes in MBF, but at high hyperemia MBV is also influential. Thus, changes in adenosine stress T2 can now be interpreted in terms of the physiological changes that led to it, enabling T2 mapping to become a viable non‐contrast option to detect ischemic myocardial tissue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".