Single‐shot steady‐state free precession can detect myocardial edema in patients: A feasibility study
Bibliographic record
Abstract
PURPOSE: To demonstrate the ability of single-shot, T(2)/T(1) weighted steady-state free precession (SSFP) to detect myocardial edema in patients with an acute myocardial infarction. MATERIALS AND METHODS: This study was performed in a series of patients (n = 10) referred for the assessment of acute myocardial infarcts (AMI). Localizers were used to obtain true short axis views of the left ventricle (LV). These views were used to plan and obtain T(2)-weighted STIR (short TI inversion recovery) images of the LV. These slices were then acquired using single-shot dark blood-prepared SSFP with a large (31) number of dummy pulses. Lastly, Contrast agent was injected, and late enhancement (LE) images were acquired. Images were analyzed using a multi-segment model of the heart. SSFP images were compared with STIR images, with STIR images used as the standard of truth for the presence of edema. LE images were used to identify segments which were positive for microvascular obstruction. RESULTS: All techniques were successful in all patients. A total of 312 segments were analyzed. Excluding segments positive for microvascular obstruction, SSFP had a sensitivity/specificity of 80%/89%. Including segments positive for microvascular obstruction, sensitivity/specificity was 71%/88%. On a patient-based analysis, no AMI was missed using SSFP (sensitivity = 100%). CONCLUSION: Using single-shot SSFP to detect myocardial edema in patients with AMI is feasible with a moderate sensitivity and high specificity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".