Cyclic variation of three-dimensional geometry of the rat carotid artery bifurcation assessed by high-frequency ultrasound imaging
Bibliographic record
Abstract
The computational simulation of the interaction between blood flow and arterial wall motion during a cardiac cycle is complicated and requires high quality information of the vessel wall motion in space and time. In this study, a set of cross-sectional ultrasound images was acquired using an electrocardiogram-gated kilohertz visualization mode, which provides 1000 frame images per second, by an ultrasound imaging system (Vevo 770, VisualSonics, Canada) with a probe of 40-MHz central frequency (RMV 704). The three dimensional (3D) geometry of carotid artery bifurcation was reconstructed at systolic and diastolic phases during a cardiac cycle using the cross-sectional ultrasound images, and a block meshing method was applied to the reconstructed 3D geometry. Then, an appropriate hexahedral mesh was constructed for the computational simulation. The 3D geometry measured by high-frequency ultrasound imaging provides high temporal and spatial information on the vessel wall motion during a cardiac cycle, which is important for accurate computational simulation of hemodynamics in the bifurcation area of the carotid artery. The fluid-structure interaction simulation of blood flow in the carotid artery bifurcation during a cardiac cycle is in progress using the meshes of 3D geometry of vessel wall. [Work supported by NIPA-2013-H0401-13-1007 and 2013R1A1A2043478.]
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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 | 0.000 |
| 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) | 0.001 | 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".