Characterization of the ultrashort‐TE (UTE) MR collagen signal
Bibliographic record
Abstract
Although current cardiovascular MR (CMR) techniques for the detection of myocardial fibrosis have shown promise, they nevertheless depend on gadolinium-based contrast agents and are not specific to collagen. In particular, the diagnosis of diffuse myocardial fibrosis, a precursor of heart failure, would benefit from a non-invasive imaging technique that can detect collagen directly. Such a method could potentially replace the need for endomyocardial biopsy, the gold standard for the diagnosis of the disease. The objective of this study was to measure the MR properties of collagen using ultrashort TE (UTE), a technique that can detect short T2* species. Experiments were performed in collagen solutions. Via a model of bi-exponential T2* with oscillation, a linear relationship (slope = 0.40 ± 0.01, R(2) = 0.99696) was determined between the UTE collagen signal fraction associated with these properties and the measured collagen concentration in solution. The UTE signal of protons in the collagen molecule was characterized as having a mean T2* of 0.75 ± 0.05 ms and a mean chemical shift of -3.56 ± 0.01 ppm relative to water at 7 T. The results indicated that collagen can be detected and quantified using UTE. A knowledge of the collagen signal properties could potentially be beneficial for the endogenous detection of myocardial fibrosis.
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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 | 0.000 | 0.001 |
| 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".