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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".