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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".