A novel magnetic resonance imaging technique to assess tissue viability following acute myocardial infarction, simultaneous T1 and T2* signal intensity monitoring following bolus injected contrast agent
Bibliographic record
Abstract
This study was designed to determine whether injured myocardium can be identified by simultaneously monitoring contrast-induced T1 and T2* signal intensity changes with an interleaved T1-T2* imaging sequence and to compare these results to techniques that calculate relaxation rate following contrast agent injection. Eight pigs were subjected to 'in situ' coronary artery occlusion for 2 hours, followed by 1-hour of reperfusion. The hearts were then excised and imaged 'ex vivo'. Gadolinium-diethylene triamine pentaacetic acid (0.05 mmol/kg) was injected as a bolus and T1 and T2* signal intensities were monitored using alternating T1 and T2*-weighted imaging to obtain simultaneous T1 and T2* timecourses during the first pass. The T2* signal at maximum T1 signal intensity displayed a percentage recovery that was significantly different (p < 0.05) between normal (30.5 +- 2.4% of baseline value), reperfused-infarcted (63 +- 7.2%), and low-reflow infarcted myocardium (90 +- 2.8%) respectively. This may reflect differences in membrane integrity between regions. On the other hand, R1 and R2* relaxation rates did not show any significant difference (p > 0.05) in the low-reflow infarcted myocardium relative to normal tissue. These results suggest that observing contrast-enhanced R1 and R2* rates early after contrast injection cannot fully differentiate viable from non-viable myocardium, but simultaneously monitoring both T1 and T2* signal intensity may help in the assessment of myocardial injury.
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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 | 0.000 |
| 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 | 0.001 |
| 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".