Longitudinal strain estimation in incompressible cylindrical tissues from magnetic resonance imaging
Bibliographic record
Abstract
In this paper, we present a simple approach for estimating the average longitudinal strains from models reconstructed from medical images. It can be used for many incompressible generalized cylindrical tissues, such as tendons, ligaments, and fusiform muscles; the major deformation directions of these soft tissues are along the longitudinal axes. The method is especially useful when pre- and post-deformation tissue correspondences are difficult to establish directly from images for various reasons, such as insufficient image resolution, homogenous image intensity, and noise. Incompressibility, which is accepted as a good approximation for soft tissues, is exploited as a constraint on the tissue deformation. Experiments with Magnetic Resonance Imaging (MRI) of tissue phantoms and computer simulations show that the method is accurate and practical even in the presence of noise. Finally, we demonstrate the usefulness of our approach on studying extraocular muscle deformation.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".