Quantification in cardiovascular magnetic resonance: agreement of software from three different vendors on assessment of left ventricular function, 2D flow and parametric mapping
Bibliographic record
Abstract
BACKGROUND: Quantitative results of cardiovascular magnetic resonance (CMR) image analysis influence clinical decision making. Image analysis is performed based on dedicated software. The manufacturers provide different analysis tools whose algorithms are often unknown. The aim of this study was to evaluate the impact of software on quantification of left ventricular (LV) assessment, 2D flow measurement and T1- and T2-parametric mapping. METHODS: , Siemens Healthineers: Argus, Medis: Qmass/Qflow) by one reader blinded to former results. Cine steady state free precession short axis images were analyzed regarding LV ejection fraction (EF), end-systolic and end-diastolic volume (ESV, EDV) and LV mass. Phase-contrast magnetic resonance images were evaluated for forward stroke volume (SV) and peak velocity (Vmax). Pixel-wise generated native T1- and T2-maps were used to assess T1- and T2-time. Forty-five data sets were evaluated twice (15 per software) for intraobserver analysis. Equivalence was considered if the confidence interval of a paired assessment of two sofware was within a tolerance interval defined by ±1.96 highest standard deviation obtained by intraobserver analysis. RESULTS: For each parameter, thirty data sets could be analyzed with all three software. All three software (A/B, A/C, B/C) were considered equivalent for LV EF, EDV, ESV, mass, 2D flow SV and T2-time. Differences between software were detected in flow measurement for Vmax and in parametric mapping for T1-time. For Vmax, equivalence was given between software A and C and for T1-time equivalence was given between software B and C. CONCLUSION: Software had no impact on quantitative results of LV assessment, T2-time and SV based on 2D flow. In contrast to that, Vmax and T1-time may be influenced by software. CMR reports should contain the name and version of the software applied for image analysis to avoid misinterpretation upon follow-up and research examinations. TRIAL REGISTRATION: ISRCTN12210850 . Registered 14 July 2017, retrospectively registered.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".