Breathing maneuvers as a coronary vasodilator for myocardial perfusion imaging
Bibliographic record
Abstract
PURPOSE: A combined breathing maneuver of hyperventilation, followed by a long voluntary breathhold leads to coronary vasodilation. We investigated the impact of breathing maneuvers on MR first-pass cardiac perfusion imaging and its potential clinical utility. MATERIALS AND METHODS: We studied 24 healthy volunteers (37 ± 12 years; 62.5% men) on a clinical 3 Tesla MRI system and performed first-pass perfusion MR at rest, during a short breathhold (S-HVBH) following 60 s of hyperventilation, and at the end of a long breathhold (L-HVBH) following the hyperventilation, performed in random order. A blinded reader analyzed signal intensity upslope, upslope index, and time between 20 and 80% of maximal signal. RESULTS: All volunteers tolerated the breathing maneuvers well and completed the study protocol. The upslope of the signal-intensity-over-time curve was increased during S-HVBH (1.86 ± 0.70 units/s, P < 0.05) and at the end of L-LVBH (1.77 ± 0.82 units/s), when compared with baseline results (1.34 ± 0.58 units/s). Corrected for the arterial input, the upslope was higher at the end of the L-HVBH (0.095 ± 0.019 units/s versus 0.077 ± 0.016 units/s at rest, P < 0.01) as was the myocardial perfusion reserve index (1.25 ± 0.22 versus 1.09 ± 0.17; P < 0.001). In a multiple regression model, only gender, rate-pressure product, and breathhold time were independently and significantly related to the upslope (R = 0.771; P < 0.001). CONCLUSION: In conclusion, a voluntary long breathhold after hyperventilation leads to an increase of the myocardial perfusion reserve index. This may impact findings from current practice of first-pass perfusion imaging. The clinical utility of breathing maneuvers as a vasodilatory stimulus for first-pass perfusion imaging may warrant further research. J. MAGN. RESON. IMAGING 2016;44:947-955.
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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 | 0.000 | 0.001 |
| 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".