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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 0.003 |
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".