Improved accuracy and precision with three‐parameter simultaneous myocardial T<sub>1</sub>and T<sub>2</sub>mapping using multiparametric SASHA
Bibliographic record
Abstract
Purpose To develop and validate a three‐parameter model for improved precision multiparametric SAturation‐recovery single‐SHot Acquisition (mSASHA) cardiac T 1 and T 2 mapping with high accuracy in a single breath‐hold. Methods The mSASHA acquisition consists of nine images of variable saturation recovery and T 2 preparation in 11 heartbeats with T 1 and T 2 values calculated using a three‐parameter model. It was validated in simulations and phantoms at 3 T with comparison to a four‐parameter joint T 1 ‐T 2 technique. The mSASHA acquisition was compared with MOLLI, SASHA, and T 2 ‐prepared balanced SSFP in 10 volunteers. Results The mSASHA technique had high accuracy in phantoms compared to spin echo, with −0.2 ± 0.3% T 1 error and −2.4 ± 1.3% T 2 error. The mSASHA coefficient of variation in phantoms for T 1 was similar to MOLLI (0.7 ± 0.2% for both) and T 2 ‐prepared balanced SSFP for T 2 (1.3 ± 0.7% vs 1.4 ± 0.3%, adjusted p > .05 for both). In simulations, three‐parameter mSASHA had higher precision than four‐parameter joint T 1 ‐T 2 for both T 1 and T 2 (46% and 11% reductions in T 1 and T 2 interquartile range for native myocardium). In vivo myocardial mSASHA T 1 was similar to SASHA (1523 ± 18 ms vs 1520 ± 18 ms) with similar coefficient of variation to both MOLLI and SASHA (3.3 ± 0.6% vs 3.1 ± 0.6% and 3.3 ± 0.5% respectively, adjusted p > .05 for all). Myocardial mSASHA T 2 was 37.1 ± 1.1 ms with similar precision to T 2 ‐prepared balanced SSFP (6.7 ± 1.7% vs 6.0 ± 1.6%, adjusted p > .05). Conclusion Three‐parameter mSASHA provides high‐accuracy cardiac T 1 and T 2 quantification in a single breath‐hold with similar precision to MOLLI and T 2 ‐prepared balanced SSFP. Further study is required to both establish normative values and demonstrate clinical utility in patient populations.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".