Saturation recovery single‐shot acquisition (SASHA) for myocardial <i>T</i><sub>1</sub> mapping
Bibliographic record
Abstract
PURPOSE: To validate a new saturation recovery single-shot acquisition (SASHA) pulse sequence for T1 mapping and to compare SASHA T1 values in heart failure patients and healthy controls. THEORY: The SASHA sequence consists of 10 electrocardiogram-triggered single-shot balanced steady-state free precession images in a breath-hold. The first image is acquired without magnetization preparation and the remaining nine images follow saturation pulses with variable saturation recovery times. METHODS: SASHA was validated through Bloch equation simulations, Monte Carlo simulations, and phantom experiments. Pre- and postcontrast myocardial and blood T1 values were measured in 29 healthy volunteers and 7 patients with heart failure. RESULTS: SASHA T1 values had excellent agreement (bias, 5 ± 5 ms) with spin echo experiments in phantoms with a wide range of physiologic T1 and T2 values and its accuracy was independent of flip angle, absolute T1 , T2 , and heart rate. The average baseline myocardial T1 in heart failure patients was higher than in healthy controls (1200 ± 32 vs. 1170 ± 9 ms, P < 0.05) at 1.5T, as was the calculated blood-tissue partition coefficient, λ, (0.42 ± 0.04 vs. 0.38 ± 0.02, P < 0.05), consistent with diffuse myocardial fibrosis. CONCLUSIONS: The SASHA sequence is a simple and fast approach to in vivo T1 mapping with good accuracy in simulations and phantom experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".