Fatty metaplasia quantification and impact on regional myocardial function as assessed by advanced cardiac MR imaging
Bibliographic record
Abstract
This study aimed to investigate the advantages of recently developed cardiac imaging techniques of fat–water separation and feature tracking to characterize better individuals with chronic myocardial infarction (MI). Twenty patients who had a previous MI underwent CMR imaging. The study protocol included routine cine and late gadolinium enhancement (LGE) technique. In addition, mDixon LGE imaging was performed in every patient. Left ventricular (LV) circumferential (Ecc LV ) and radial (Err LV ) strain were calculated using dedicated software (CMR 42 , Circle, Calgary, Canada). The extent of global scar was measured in LGE and fat–water separated images to compare conventional and recent CMR imaging techniques. The infarct size derived from conventional LGE and fat–water separated images was similar. However, detection of lipomatous metaplasia was only possible with mDixon imaging. Subjects with fat deposition demonstrated a significantly smaller percentage of fibrosis than those without fat (10.68 ± 5.07% vs. 13.83 ± 6.30%; p = 0.005). There was no significant difference in Ecc LV or Err LV between myocardial segments containing fibrosis only and fibrosis with fat. However, Ecc LV and Err LV values were significantly higher in myocardial segments adjacent to fibrosis with fat deposition than in those adjacent to LGE only. Advanced CMR imaging ensures more detailed tissue characterization in patients with chronic MI without a relevant increase in imaging and post-processing time. Fatty metaplasia may influence regional myocardial deformation especially in the myocardial segments adjacent to scar tissue. A simplified and shortened myocardial viability CMR protocol might be useful to better characterize and stratify patients with chronic MI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".