Vascular wall stress during intravascular optical coherence tomography imaging
Bibliographic record
Abstract
Biomechanical properties of arterial wall is crucial for understanding the changes in the cardiovascular system. Catheters are used during intravascular optical coherence tomography (IVOCT) imaging. The presence of a catheter alters the flow field, pressure distribution and frictional resistance to flow in an artery. In this paper, we first study the transmural stress distribution of the catheterized vessel. COMSOL (COMSOL 4.4) was used to simulate the blood flow induced deformation in a catheterized vessel. Blood is modeled as an incompressible Newtonian fluid. Stress distribution from an three-layer vascular model with an eccentric catheter are simulated, which provides a general idea about the distribution of the displacement and the stress. Optical coherence elastography techniques were then applied to porcine carotid artery samples to look at the deformation status of the vascular wall during saline or water injection. Preliminary simulation results show nonuniform stress distribution in the circumferential direction of the eccentrically catheterized vascular model. Three strain rate methods were tested for intravascular OCE application. The tissue Doppler method has the potential to be further developed to image the vascular wall biomechnical properties in vivo. Although results in this study are not validated quantitatively, the experiments and methods may be valuable for intravascular OCE studies, which may provide important information for cardiovascular disease prevention, diagnosis and treatment.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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 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".