High-Resolution 3-D T${{\bf _1}^{\bf *}}$-Mapping and Quantitative Image Analysis of <i>GRAY ZONE</i> in Chronic Fibrosis
Bibliographic record
Abstract
The substrate of potentially lethal cardiac arrhythmias often resides in the gray zone (GZ), a mixture of viable myocytes and collagen strands found between healthy myocardium and infarct core (IC). The specific aims of this paper are to demonstrate correspondence between regions delineated in T1* (apparent T1) maps and tissue characteristics seen in histopathology and to determine the MR imaging resolution needed to adequately identify GZ-associated substrate in chronic infarct. For this, a novel 3-D multicontrast late enhancement (MCLE) MR method was used to image ex vivo swine hearts with chronic infarction, at high resolution ( 0.6×0.6×1.25 mm). Pixel-wise classified tissue maps were calculated using steady-state and T1* images as input to a fuzzy-clustering algorithm. Quantitative histology based on collagen stains was performed in n = 10 selected slabs and showed very good correlations between histologically-determined areas of heterogeneous and dense fibrosis, and the corresponding GZ ( R2 = 0.96) and IC ( R2 = 0.97 ) in tissue classified maps. Furthermore, in n = 24 slabs, we performed volumetric measurements of GZ and IC, at the original and decreased image resolutions. Our results demonstrated that the IC volume remained relatively unchanged across all resolutions, whereas the GZ volume progressively increased with diminished image resolution, with changes reaching significance at 1×1×5 mm resolution (p < 0.05 ) but not at 1×1×2.5 mm, suggesting that this resolution may be sufficient to adequately identify the GZ from MCLE images, enabling an effective MR probing of remodeled myocardium in late infarct. Future work will focus on translating these findings to optimizing the current in vivo MCLE imaging of the GZ.
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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) | 0.000 | 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".