Workflow Comparison for Combined 4D MRI/CFD Patient-Specific Cardiovascular Flow Simulations of the Thoracic Aorta
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Computational fluid dynamics (CFD) has been widely used to predict and understand cardiovascular flows. However, the accuracy of CFD predictions depends on faithful reconstruction of patient vascular anatomy and accurate patient-specific inlet and outlet boundary conditions. 4-Dimensional magnetic resonance imaging (4D MRI) can provide patient-specific data to obtain the required geometry and time-dependent flow boundary conditions for CFD simulations, and can further be used to validate CFD predictions. This work presents a framework to combine both spatiotemporal 4D MRI data and patient monitoring data with CFD simulation workflows. To assist practitioners, all aspects of the modeling workflow, from geometry reconstruction to results postprocessing, are illustrated and compared using three software packages (ansys, comsol, SimVascular) to predict hemodynamics in the thoracic aorta. A sensitivity analysis with respect to inlet boundary condition is presented. Results highlight the importance of 4D MRI data for improving the accuracy of flow predictions on the ascending aorta and the aortic arch. In contrast, simulation results for the descending aorta are less sensitive to the patient-specific inlet boundary conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 it