A New approach towards improved visualization of myocardial edema using T2‐weighted imaging: A cardiovascular magnetic resonance (CMR) study
Bibliographic record
Abstract
PURPOSE: To compare T2-weighted cardiovascular magnetic resonance (CMR) imaging with AASPIR (asymmetric adiabatic spectral inversion recovery) and STIR (short T1 inversion recovery) for myocardial signal intensity, image quality, and fat suppression. MATERIALS AND METHODS: Forty consecutive patients (47 ± 16 years old) referred by cardiologists for CMR-based myocardial tissue characterization were scanned with both STIR and AASPIR T2-weighted imaging approaches. Signal intensity of left ventricular myocardium was normalized to a region of interest generating a signal-to-noise ratio (SNR). In six patients with regional edema on STIR the contrast-to-noise ratio (CNR) was assessed. Two independent observers used a scoring system to evaluate image quality and artifact suppression. Six healthy volunteers (three males, 32 ± 7 years) were recruited to compare fat suppression between AASPIR and STIR. RESULTS: SNR of AASPIR was greater than STIR for basal (128 ± 44 vs. 83 ± 40, P < 0.001), mid- (144 ± 65 vs. 96 ± 39, P < 0.01), and apical (145 ± 59 vs. 105 ± 35, P < 0.05) myocardium. Improved image quality and greater suppression of artifacts was demonstrated with AASPIR. In patients with regional edema, CNR increased by 49% with AASPIR, while SNR of pericardial fat did not differ (44 ± 39 vs. 33 ± 30, P > 0.05). CONCLUSION: Our findings support the implementation of an AASPIR-based approach for T2-weighted imaging due to improved pericardial fat suppression, image quality, and artifact suppression with greater CNR and SNR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".