Effects of slice orientation on reproducibility of sequential assessment of right ventricular volumes and ejection fraction: short-axis vs transverse SSFP cine cardiovascular magnetic resonance
Bibliographic record
Abstract
BACKGROUND: Test-retest reproducibility is of utmost importance in follow-up of right ventricular (RV) volumes and function; optimal slice orientation though is not yet known. We compared test-retest reproducibility and intra-/inter-observer variability of right ventricular (RV) volumes and function assessed with short-axis and transverse cardiovascular magnetic resonance (CMR). METHODS: Eighteen volunteers underwent cine CMR for RV assessment obtaining ventricular coverage in short-axis and transverse slice orientation. Additional 2D phase contrast flow imaging of the main pulmonary artery (MPA) was performed. After complete repositioning repeat acquisitions were performed. Data sets were contoured by two blinded observers. Statistical analysis included Student's t-test, Bland-Altman plots, intra-class correlation coefficient (ICC) and 2-way ANOVA, SEM and minimal detectable difference calculations. RESULTS: Heart rates (65.0 ± 7.4 vs. 67.6 ± 9.9 bpm; P = 0.1) and MPA flow (89.8 ± 16.6 vs. 87.2 ± 14.9 mL; P = 0.1) did not differ between imaging sessions. EDV and ESV demonstrated an inter-study bias of 0.4 %[-9.5 %,10.3 %] and 2.1 %[-12.3 %,16.4 %] for short-axis and 1.1 %[-7.3 %,9.4 %] and 0.8 %[-16.0 %,17.6 %] for transverse orientation, respectively. There was no significant interaction between imaging orientation and interstudy reproducibility (p = 0.395-0.824), intra-observer variability (p = 0.726-0.862) or inter-observer variability (p = 0.447-0.706) by 2-way ANOVA. Inter-observer agreement by ICC was greater for short axis versus transverse orientation for all parameters (0.769-0.986 vs. 0.625-0.983, respectively). Minimal detectable differences for short axis and transverse orientations were 10.1 mL/11.5 mL for EDV, 8.3 mL/8.4 mL for ESV and 4.1 % vs. 4.7 % for EF, respectively. CONCLUSION: Short-axis and transverse orientation both provide reliable and reproducible measures for follow-up of RV volumes and global function. Therefore, additional transverse SSFP cine CMR may not necessarily be required if performed for the sole purpose of quantitative volumetric RV assessment.
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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 | 0.016 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".