11637-Tesla Cardiac MRI for Ventricular and Valvular quantitation in healthy volunteers
Bibliographic record
Abstract
Background: Ultra-high-field (B0 ≥7 Tesla) cardiovascular magnetic resonance (CMR) offers increased resolution, but cardiac imaging requiring ECG gating is significantly impacted from the magneto-hydrodynamic (MHD) effect, distorts the ECG trace (1–3). Previously, 7T CMR was constrained to using pulse oximetry for triggering, We explored the technical feasbility of a 7 T research MR scanner using of-the-art ECG trigger algorithm to assess left and right ventricular volumes, aortic and pulmonary valve flow. Methods: 7T CMR scans were performed on 10 healthy volunteers on whole-body research MRI scanner (Siemens Healthcare, Erlangen, Germany) with 8 channel Tx/32 channel Rx cardiac coil (MRI Tools GmbH, Berlin, Germany) under institutional review board approval. Vectorcardiogram ECG was successfully performed using a learning phase outside of the magnetic field, with a trigger algorithm with sufficient accuracy for CMR despite severe ECG signal distortions from the 7T field. Cine CMR was performed after 3rd-order B0 shimming using a high-resolution breath-held ECG-retro-gated segmented two-dimensional spoiled gradient echo sequence, and 2-dimensional phase contrast flow imaging. Analysis was performed using Cmr42 software (Circle CVi, Calgary).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".