Free‐breathing simultaneous myocardial T<sub>1</sub> and T<sub>2</sub> mapping with whole left ventricle coverage
Bibliographic record
Abstract
Purpose To develop a free‐breathing sequence, that is, Multislice Joint T 1 ‐T 2 , for simultaneous measurement of myocardial T 1 and T 2 for multiple slices to achieve whole left‐ventricular coverage. Methods Multislice Joint T 1 ‐T 2 adopts slice‐interleaved acquisition to collect 10 single‐shot electrocardiogram‐triggered images for each slice prepared by saturation and T 2 preparation to simultaneously estimate myocardial T 1 and T 2 and achieve whole left‐ventricular coverage. Prospective slice‐tracking using a respiratory navigator and retrospective image registration are used to reduce through‐plane and in‐plane motion, respectively. Multislice Joint T 1 ‐T 2 was validated through numerical simulations and phantom and in vivo experiments, and compared with saturation‐recovery single‐shot acquisition and T 2 ‐prepared balanced Steady‐State Free Precession (T 2 ‐prep SSFP) sequences. Results Phantom T 1 and T 2 from Multislice Joint T 1 ‐T 2 had good accuracy and precision, and were insensitive to heart rate. Multislice Joint T 1 ‐T 2 yielded T 1 and T 2 maps of nine left‐ventricular slices in 1.4 minutes. The mean left‐ventricular T 1 difference between saturation‐recovery single‐shot acquisition and Multislice Joint T 1 ‐T 2 across healthy subjects and patients was 191 ms (1564 ± 60 ms versus 1373 ± 50 ms; P < .05) and 111 ms (1535 ± 49 ms vs 1423 ± 49 ms; P < .05), respectively. The mean difference in left‐ventricular T 2 between T 2 ‐prep SSFP and Multislice Joint T 1 ‐T 2 across healthy subjects and patients was −6.3 ms (42.4 ± 1.4 ms vs 48.7 ± 2.5; P < .05) and −5.7 ms (41.6 ± 2.5 ms vs 47.3 ± 2.7; P < .05), respectively. Conclusion Multislice Joint T 1 ‐T 2 enables quantification of whole left‐ventricular T 1 and T 2 during free breathing within a clinically feasible scan time of less than 2 minutes.
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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 | 0.004 |
| 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".