Accuracy, Precision, and Reproducibility of Four T1 Mapping Sequences: A Head-to-Head Comparison of MOLLI, ShMOLLI, SASHA, and SAPPHIRE
Bibliographic record
Abstract
PURPOSE: To compare accuracy, precision, and reproducibility of four commonly used myocardial T1 mapping sequences: modified Look-Locker inversion recovery (MOLLI), shortened MOLLI (ShMOLLI), saturation recovery single-shot acquisition (SASHA), and saturation pulse prepared heart rate independent inversion recovery (SAPPHIRE). MATERIALS AND METHODS: This HIPAA-compliant study was approved by the institutional review board. All subjects provided written informed consent. Accuracy, precision, and reproducibility of the four T1 mapping sequences were first compared in phantom experiments. In vivo analysis was performed in seven healthy subjects (mean age ± standard deviation, 38 years ± 19; four men, three women) who were imaged twice on two separate days. In vivo reproducibility of native T1 mapping and extracellular volume (ECV) were measured. Differences between the sequences were assessed by using Kruskal-Wallis and Wilcoxon rank sum tests (phantom data) and mixed-effect models (in vivo data). RESULTS: T1 mapping accuracy in phantoms was lower with ShMOLLI (62 msec) and MOLLI (44 msec) than with SASHA (13 msec; P < .05) and SAPPHIRE (12 msec; P < .05). MOLLI had similar precision to ShMOLLI (4.0 msec vs 5.6 msec; P = .07) but higher precision than SAPPHIRE (6.8 msec; P = .002) and SASHA (8.7 msec; P < .001). All sequences had similar reproducibility in phantoms (P = .1). The four sequences had similar in vivo reproducibility for native T1 mapping (∼25-50 msec; P > .05) and ECV quantification (∼0.01-0.02; P > .05). CONCLUSION: SASHA and SAPPHIRE yield higher accuracy, lower precision, and similar reproducibility compared with MOLLI and ShMOLLI for T1 measurement. Different sequences yield different ECV values; however, all sequences have similar reproducibility for ECV quantification.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".