Tools for experimental characterization of the non-uniform rotational distortion in intravascular OCT probes
Bibliographic record
Abstract
The Industrial Material Institute (IMI) together with the Institute for Biodiagnostic (IBD) has developed its own optical catheters for cardiovascular imaging applications. Those catheters have been used experimentally in the in vitro coronary artery model of the Langendorff beating heart and in a percutaneous coronary intervention procedure in a porcine model. For some catheter designs, non-uniform rotational distortion (NURD) can be observed as expected from past experience with intra-vascular ultrasound (IVUS) catheters. A two-dimensional (2D) coronary artery test bench that simulates the path into the coronary arteries has been developed. The presence or absence of NURD can be assessed with the test bench using a custom-built cardiovascular Optical Coherence Tomography (OCT) imaging system. A square geometry instead of the circular shape of an artery is used to simulate the coronary arteries. Thereby, it is easier to visualize NURD when it is present. The accumulated torsion induced by the friction on the catheter is measured along the artery path. NURD is induced by the varying friction force that is balanced by the accumulated torsion force. The pullback force is measured and correlated with NURD observed in the 2D test bench. Finally, a model is presented to help understanding the mechanical constraint that leads to the friction force variations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".