Inversion‐recovery‐prepared SSFP for cardiac‐phase‐resolved delayed‐enhancement MRI
Bibliographic record
Abstract
Delayed-enhancement magnetic resonance imaging (DE-MRI) can be used to visualize myocardial infarction (MI). DE-MRI is conventionally acquired with an inversion-recovery gradient-echo (IR-GRE) pulse sequence that yields a single bright-blood image. IR-GRE imaging requires an accurate estimate of the inversion time (TI) to null the signal from the myocardium, and a separate cine acquisition is required to visualize myocardial wall motion. Simulations were performed to examine the effects of a steady-state free precession (SSFP) readout after an inversion pulse in the setting of DE-MRI. Using these simulations, a segmented IR-SSFP sequence was optimized for infarct visualization. This sequence yields both viability and wall motion images over the cardiac cycle in a single breath-hold. Viability images at multiple effective TIs are produced, providing a range of image contrasts. In a study of 11 patients, IR-SSFP yielded infarct sizes and left ventricular ejection fractions (LVEFs) similar to those obtained by IR-GRE and standard SSFP, respectively. IR-SSFP images yielded improved visualization of the infarct-blood border because of the simultaneous nulling of healthy myocardium and blood. T(1) (*) recovery curves were extracted from IR-SSFP images and showed excellent qualitative agreement with theoretical simulations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".