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 T1‐T2, for simultaneous measurement of myocardial T1 and T2 for multiple slices to achieve whole left‐ventricular coverage. Methods Multislice Joint T1‐T2 adopts slice‐interleaved acquisition to collect 10 single‐shot electrocardiogram‐triggered images for each slice prepared by saturation and T2 preparation to simultaneously estimate myocardial T1 and T2 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 T1‐T2 was validated through numerical simulations and phantom and in vivo experiments, and compared with saturation‐recovery single‐shot acquisition and T2‐prepared balanced Steady‐State Free Precession (T2‐prep SSFP) sequences. Results Phantom T1 and T2 from Multislice Joint T1‐T2 had good accuracy and precision, and were insensitive to heart rate. Multislice Joint T1‐T2 yielded T1 and T2 maps of nine left‐ventricular slices in 1.4 minutes. The mean left‐ventricular T1 difference between saturation‐recovery single‐shot acquisition and Multislice Joint T1‐T2 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 T2 between T2‐prep SSFP and Multislice Joint T1‐T2 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 T1‐T2 enables quantification of whole left‐ventricular T1 and T2 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 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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".