11 - Modélisation et suivi par modèle d'état harmonique du mouvement ventriculaire gauche du coeur en Imagerie par Résonance Magnétique
Bibliographic record
Abstract
In this article, we propose a new method for modeling the left ventricular motion of the heart from a magnetic resonance imaging (MRI) sequence. We propose to model the space-time trajectory of the points of the endocardial (respectively epicardial) contour of the left ventricle (LV) using a harmonic model of movement, which is linear and can describe the dynamics of the left ventricle throughout the cardiac cycle. This new model is based on the assumption of quasi-periodicity of the cardiac cycle and uses a Kalman filter as estimation tool. We first refer to the main works in the field, then describing our method. We give the way to get the space-time trajectories of the contour points of the LV. We present the model with the selected state equations and the Kalman filter based motion estimate. We propose two methods of calculation. The direct one provides a solution for a fixed rank of the harmonic model. The recursive one allows progressively go from rank n to rank n + 1 without prior choice. The model is validated on simulated data by direct comparison with the traditional Fourier decomposition approach. It is shown that it fits well the studied trajectories. The results obtained on real cardiac sequences are particularly interesting because they demonstrate the capability of our method to discriminate unambiguously normal cases from pathological cases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".