Quantification of changes in myocardial <scp>T<sub>1</sub></scp>* values with exercise cardiac <scp>MRI</scp> using a free‐breathing <scp>non‐electrocardiograph</scp> radial imaging
Bibliographic record
Abstract
Purpose To develop and evaluate a free breathing non‐electrocardiograph (ECG) myocardial T1* mapping sequence using radial imaging to quantify the changes in myocardial T1* between rest and exercise (T1*reactivity) in exercise cardiac MRI (Ex‐CMR). Methods A free‐running T1* sequence was developed using a saturation pulse followed by three Look‐Locker inversion‐recovery experiments. Each Look‐Locker continuously acquired data as radial trajectory using a low flip‐angle spoiled gradient‐echo readout. Self‐navigation was performed with a temporal resolution of ∼100 ms for retrospectively extracting respiratory motion. The mid‐diastole phase for every cardiac cycle was retrospectively detected on the recorded electrocardiogram signal using an empirical model. Multiple measurements were performed to obtain mean value to reduce effects from the free‐breathing acquisition. Finally, data acquired at both mid‐diastole and end‐expiration are picked and reconstructed by a low‐rank plus sparsity constraint algorithm. The performance of this sequence was evaluated by simulations, phantoms, and in vivo studies at rest and after physiological exercise. Results Numerical simulation demonstrated that changes in T1* are related to the changes in T1; however, other factors such as breathing motion could influence T1* measurements. Phantom T1* values measured using free‐running T1* mapping sequence had good correlation with spin‐echo T1 values and was insensitive to heart rate. In the Ex‐CMR study, the measured T1* reactivity was 10% immediately after exercise and declined over time. Conclusion The free‐running T1* mapping sequence allows free‐breathing non‐ECG quantification of changes in myocardial T1* with physiological exercise. Although, absolute myocardial T1* value is sensitive to various confounders such as B1 and B0 inhomogeneity, quantification of its change may be useful in revealing myocardial tissue properties with exercise.
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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) | 0.000 | 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".