Myocardial <i>T</i><sub>1</sub> mapping: Application to patients with acute and chronic myocardial infarction
Bibliographic record
Abstract
T(1) maps obtained with modified Look-Locker inversion recovery (MOLLI) can be used to measure myocardial T(1). We aimed to evaluate the potential of MOLLI T(1) mapping for the assessment of acute and chronic myocardial infarction (MI). A total of 24 patients with a first MI underwent MRI within 8 days and after 6 months. T(1) mapping was performed at baseline and at selected intervals between 2-20 min following administration of gadopentetate dimeglumine (Gd-DTPA). Delayed-enhancement (DE) imaging served as the reference standard for delineation of the infarct zone. On T(1) maps the myocardial T(1) relaxation time was assessed in hyperenhanced areas, hypoenhanced infarct cores, and remote myocardium. The planimetric size of myocardial areas with standardized T(1) threshold values was measured. Acute and chronic MI exhibited different T(1) changes. Precontrast threshold T(1) maps detected segmental abnormalities caused by acute MI with 96% sensitivity and 91% specificity. Agreement between measurements of infarct size from T(1) mapping and DE imaging was higher in chronic than in acute infarcts. Precontrast T(1) maps enable the detection of acute MI. Acute and chronic MI show different patterns of T(1) changes. Standardized T(1) thresholds provide the potential to dichotomously identify areas of infarction.
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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 | 0.002 |
| 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 | 0.000 | 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".