Reliability of respiratory-triggered two-dimensional cine k-adaptive-t-autocalibrating reconstruction for Cartesian sampling for the assessment of biventricular volume and function in patients with repaired tetralogy of Fallot
Bibliographic record
Abstract
Objective: To compare left ventricular (LV) and right ventricular (RV) volume, function, and image quality of a respiratory-triggered two-dimensional (2D)-cine k-adaptive-t-autocalibrating reconstruction for Cartesian sampling (2D kat-ARC) with those of the standard reference, namely, breath-hold 2D balanced steady-state free precession (2D SSFP), in patients with repaired tetralogy of Fallot (TOF). Methods: 30 patients (14 males, mean age 32.2 ± 13.9 years) underwent cardiac magnetic resonance, and 2D kat-ARC and 2D SSFP images were acquired on short-axis view. Biventricular end-diastolic volume (EDV) and end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), and LV mass (LVM) were analysed. Results: The 2D kat-ARC had significantly shorter scan time (35.2 ± 9.1 s vs 80.4 ± 16.7 s; p < 0.0001). Despite an analysis of image quality showed significant impairment using 2D kat-ARC compared to 2D SSFP cine (p < 0.0001), the two sequences demonstrated no significant difference in terms of biventricular EDV, LVESV, LVSV, LVEF, and LVM. However, the RVESV was overestimated for 2D kat-ARC compared with that for 2D SSFP (73.8 ± 43.2 ml vs 70.3 ± 44.5 ml, p = 0.0002) and the RVSV and RVEF were underestimated (RVSV = 46.2±20.5 ml vs 49.4 ± 20.4 ml, p = 0.0024; RVEF = 40.2±12.7% vs. 43.5±14.0%, p = 0.0002). Conclusion: Respiratory-triggered 2D kat-ARC cine is a reliable technique that could be used in the evaluation of LV volumes and function. Advances in knowledge: 2D cine kat-ARC is a reliable technique for the assessment LV volume and function in patients with repaired TOF.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".