Accuracy of Right and Left Ventricular Functional Assessment by Short-Axis vs Axial Cine Steady-State Free-Precession Magnetic Resonance Imaging: Intrapatient Correlation with Main Pulmonary Artery and Ascending Aorta Phase-Contrast Flow Measurements
Bibliographic record
Abstract
OBJECTIVE: The left ventricle (LV) is routinely assessed with cardiac magnetic resonance imaging (MRI) by using short-axis orientation; it remains unclear whether the right ventricle (RV) can also be adequately assessed in this orientation or whether dedicated axial orientation is required. We used phase-contrast (PC) flow measurements in the main pulmonary artery (MPA) and the ascending aorta (Aorta) as nonvolumetric standard of reference and compared RV and LV volumes in short-axis and axial orientations. METHODS: A retrospective analysis identified 30 patients with cardiac MRI data sets. Patients underwent MRI (1.5 T or 3 T), with retrospectively gated cine steady-state free-precession in axial and short-axis orientations. PC flow analyses of MPA and Aorta were used as the reference measure of RV and LV output. RESULTS: There was a high linear correlation between MPA-PC flow and RV-stroke volume (SV) short axis (r = 0.9) and RV-SV axial (r = 0.9). Bland-Altman analysis revealed a mean offset of 1.4 mL for RV axial and -2.3 mL for RV-short-axis vs MPA-PC flow. There was a high linear correlation between Aorta-PC flow and LV-SV short-axis (r = 0.9) and LV-SV axial (r = 0.9). Bland-Altman analysis revealed a mean offset of 4.8 m for LV short axis and 7.0 mL for LV axial vs Aorta-PC flow. There was no significant difference (P = .6) between short-axis-LV SV and short-axis-RV SV. CONCLUSION: No significant impact of the slice acquisition orientation for determination of RV and LV stroke volumes was found. Therefore, cardiac magnetic resonance workflow does not need to be extended by an axial data set for patients without complex cardiac disease for assessment of biventricular function and volumes.
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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 | 0.003 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".