Multi‐contrast volumetric imaging with isotropic resolution for assessing infarct heterogeneity: Initial clinical experience
Bibliographic record
Abstract
Background. To evaluate accelerated multi‐contrast volumetric imaging with isotropic resolution reconstructed using low‐rank and spatially varying edge‐preserving constrained compressed sensing parallel imaging reconstruction (CP‐LASER), for assessing infarct heterogeneity on post‐infarction patients as a precursor to studies of utility for predicting ventricular arrhythmias. Methods. Eleven patients with prior myocardial infarction were included in the study. All subjects underwent cardiovascular magnetic resonance (CMR) scans including conventional two‐dimensional late gadolinium enhancement (2D LGE) and three‐dimensional multi‐contrast late enhancement (3D MCLE) post‐contrast. The extent of the infarct core and peri‐infarct gray zone of a limited mid‐ventricular slab were derived respectively by analyzing MCLE images with an isotropic resolution of 2.2 mm and an anisotropic resolution of mm , and LGE images with a resolution of mm ; the respective measures across all subjects were statistically compared. Results. Using 3D MCLE, the infarct core size measured with isotropic resolution was similar to that measured with anisotropic resolution, while the peri‐infarct gray zone size measured with isotropic resolution was smaller than that measured with anisotropic resolution ( , Cohen's ). Isotropic 3D MCLE yielded a significantly smaller measure of the peri‐infarct gray zone size than conventional 2D LGE ( , Cohen's ). Overall, we have successfully shown the utility of isotropic 3D MCLE in a pilot patient study. Our results suggest that smaller voxels lead to more accurate differentiation between isotropic 3D MCLE‐derived gray zone and core infarct because of diminished partial volume effect. Conclusion. The CP‐LASER accelerated 3D MCLE with isotropic resolution can be used in patients and yields excellent delineation of infarct and peri‐infarct gray zone characteristics.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".