Feasibility Study of Computational Fluid Dynamics Simulation of Coronary Computed Tomography Angiography Based on Dual-Source Computed Tomography
Bibliographic record
Abstract
BACKGROUND: Adding functional features to morphological features offers a new method for non-invasive assessment of myocardial perfusion. This study aimed to explore technical routes of assessing the left coronary artery pressure gradient, wall shear stress distribution and blood flow velocity distribution, combining three-dimensional coronary model which was based on high resolution dual-source computed tomography (CT) with computational fluid dynamics (CFD) simulation. METHODS: Three cases of no obvious stenosis, mild stenosis and severe stenosis in left anterior descending (LAD) were enrolled. Images acquired on dual-source CT were input into software Mimics, ICEMCFD and FLUENT to simulate pressure gradient, wall shear stress distribution and blood flow velocity distribution. Measuring coronary enhancement ratio of coronary artery was to compare with pressure gradient. RESULTS: Results conformed to theoretical values and showed difference between normal and abnormal samples. CONCLUSIONS: The study verified essential parameters and basic techniques in blood flow numerical simulation preliminarily. It was proved feasible.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".