Quantitative myocardial blood flow and perfusion reserve with exercise cardiovascular magnetic resonance
Bibliographic record
Abstract
BACKGROUND: Myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) can be quantified using vasodilator stress cardiovascular magnetic resonance (CMR). Exercise stress CMR (Ex-CMR) offers a more physiological assessment of cardiac functional reserve. While visual interpretation of Ex-CMR perfusion has been successfully applied, the feasibility of quantitative Ex-CMR perfusion remains unproven. We aimed to assess the feasibility of quantitative Ex-CMR perfusion imaging for characterizing exercise-induced perfusion responses and to perform a pilot study comparing MBF and MPR among patients with hypertrophic cardiomyopathy (HCM), heart failure with preserved ejection fraction (HFpEF), and non-cardiac dyspnea (NCD). METHODS: In this prospective study, patients with HCM, HFpEF, or NCD underwent Ex-CMR at 3T using a supine ergometer. Exercise was performed outside the scanner bore, followed by stress perfusion imaging 45-60 s post-exercise and rest perfusion 5-7 min later. A dual-sequence protocol with inline pixel-wise quantification was used to calculate MBF and MPR. Image quality and feasibility were visually assessed. Group comparisons were performed using analysis of variance and t-tests; linear regression was used to explore clinical associations. RESULTS: Of 108 patients enrolled, 9 were excluded due to obstructive coronary artery disease or reduced ejection fraction. Quantitative Ex-CMR was successful (at least one analyzable paired rest and post-exercise slice) in 90% (10/99) of cases. Most frequent quality issues were inadequate gating or arrhythmias and slice misalignment. The final cohort included 89 patients: 34 HCM, 34 HFpEF, and 21 NCD. Patients with HCM showed significantly lower MBF and MPR than HFpEF and NCD (MBF: 1.03 ± 0.27 vs 1.25 ± 0.40 and 1.13 ± 0.25 mL/min/g; MPR: 1.27 ± 0.21 vs 1.41 ± 0.29 and 1.44 ± 0.22; all p < 0.05). Peak exercise heart rate was the strongest independent predictor of MBF (β = 0.009, p < 0.001) and MPR (β = 0.004, p = 0.022). CONCLUSION: Ex-CMR quantitative MBF and MPR assessment is feasible in most patients after image quality control. While the increase in MBF was limited during low-to-moderate exercise intensity in this pilot study, Ex-CMR revealed distinct perfusion response patterns among studied cohorts.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".