Inputs for Subject-Specific Computational Fluid Dynamics Simulation of Blood Flow in the Mouse Aorta
Bibliographic record
Abstract
Mouse models are an important way for exploring relationships between blood hemodynamics and eventual plaque formation. We have developed a mouse model of aortic regurgitation (AR) that produces large changes in plaque burden with charges in hemodynamics [Zhou et al., 2010, "Aortic Regurgitation Dramatically Alters the Distribution of Atherosclerotic Lesions and Enhances Atherogenesis in Mice," Arterioscler. Thromb. Vasc. Biol., 30(6), pp. 1181-1188]. In this paper, we explore the amount of detail needed for realistic computational fluid dynamics (CFD) calculations in this experimental model. The CFD calculations use inputs based on experimental measurements from ultrasound (US), micro computed tomography (CT), and both anatomical magnetic resonance imaging (MRI) and phase contrast MRI (PC-MRI). The adequacy of five different levels of model complexity (a) subject-specific CT data from a single mouse; (b) subject-specific CT centerlines with radii from US; (c) same as (b) but with MRI derived centerlines; (d) average CT centerlines and averaged vessel radius and branching vessels; and (e) same as (d) but with averaged MRI centerlines) is evaluated by demonstrating their impact on relative residence time (RRT) outputs. The paper concludes by demonstrating the necessity of subject-specific geometry and recommends for inputs the use of CT or anatomical MRI for establishing the aortic centerlines, M-mode US for scaling the aortic diameters, and a combination of PC-MRI and Doppler US for estimating the spatial and temporal characteristics of the input wave forms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".