Evaluation of knowledge-based reconstruction for magnetic resonance volumetry of the right ventricle in tetralogy of fallot
Bibliographic record
Abstract
Purpose: Evaluating right ventricular (RV) volumes and function is important in the clinical management of patients after tetralogy of Fallot (TOF) repair. Currently, cardiac magnetic resonance (CMR) using Simpson's method is the gold standard for RV quantitative assessment. However, this method is time consuming and not without sources of error. Knowledge-based reconstruction (KBR) is a new imaging tool for RV volumetry and has been recently validated on echocardiography. The aim of this study was to assess the feasibility, accuracy, and labor intensity of KBR on CMR datasets in a group of repaired TOF patients by comparison with measurements obtained by Simpson's method. Methods: Thirty five patients (mean age 14±3 years) after TOF repair were studied using KBR and Simpson's method. Parameters analyzed were RV end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF) and post-processing time. All measurements were compared with the standard Simpson's method. Intraobserver, interobserver and intermethod variability was assessed using Pearson's correlation analysis, coefficients of variation and Bland-Altman analysis. Results: KBR was feasible and highly accurate as compared to Simpson's method. Intra- and intermethod variability for KBR measurements showed good agreements. When compared with Simpson's method, volumetry using KBR was faster (10.9±2.0 vs. 7.1±2.4 minutes, P<.001, respectively). Projection of the 3D model on a 2D image Conclusion: In repaired TOF patients, KBR is a feasible, accurate and reproducible method for measuring RV volumes and function. In addition, the post-processing time of RV volumetry using KBR was significantly shorter when compared with Simpson's method.
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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 | 0.013 |
| 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 | 0.000 |
| 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".