Comparison of long and short axis quantification of left ventricular volume parameters by cardiovascular magnetic resonance, with ex-vivo validation
Bibliographic record
Abstract
BACKGROUND: The purpose of the study was to compare the accuracy and evaluation time of quantifying left ventricular (LV), left atrial (LA) volume and LV mass using short axis (SAX) and long axis (LAX) methods when using cardiovascular magnetic resonance (CMR). MATERIALS AND METHODS: We studied 12 explanted canine hearts and 46 patients referred for CMR (29 male, age 47 ± 18 years) in a clinical 1.5 T CMR system, using standard cine sequences. In standard short axis stacks of various slice thickness values in dogs and 8 mm slice thickness (gap 2 mm) in patients, we measured LV volumes using reference slices in a perpendicular, long axis orientation using certified software. Volumes and mass were also measured in six radial long axis (LAX) views.LV parameters were also assessed for intra- and inter-observer variability. In 24 patients, we also analyzed reproducibility and evaluation time of two very experienced (> 10 years of CMR reading) readers for SAX and LAX. RESULTS: In the explanted dog hearts, there was excellent agreement between ex vivo data and LV mass and volume data as measured by all methods for both, LAX (r² = 0.98) and SAX (r² = 0.88 to 0.98). LA volumes, however, were underestimated by 13% using the LAX views. In patients, there was a good correlation between all three assessed methods (r² ≥ 0.95 for all). In experienced clinical readers, left-ventricular volumes and ejection fraction as measured in LAX views showed a better inter-observer reproducibility and a 27% shorter evaluation time. CONCLUSION: When compared to an ex vivo standard, both, short axis and long axis techniques are highly accurate for the quantification of left ventricular volumes and mass. In clinical settings, however, the long axis approach may be more reproducible and more time-efficient. Therefore, the rotational long axis approach is a viable alternative for the clinical assessment of cardiac volumes, function and mass.
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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 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".