Combining 4D MRI With CFD for Investigating Patient-Specific Cardiovascular Flows: A Comprehensive Comparison of ANSYS, COMSOL, and SimVascular Illustrated With the Prediction of Thoracic Aortic Hemodynamics
Bibliographic record
Abstract
Abstract Computational Fluid Dynamics (CFD) is widely used in both scientific and industrial contexts to provide valuable insights into cardiovascular flows. CFD supports the development of medical devices, describes complex flow physical phenomena associated with disease generation and progression, and even informs surgical planning. Non-invasive technologies such as 4D MRI provide detailed information about blood flow for a given patient, yet CFD allows higher spatial and temporal resolution less invasively. However, the advantages of CFD methods can only be realized through faithful geometry reconstruction, high-quality mesh generation, and suitable definition of patient-specific inlet and outlet boundary conditions. In this regard, 4D MRI measurements can provide the required data to calibrate and validate patient-specific CFD models. Hence, the combination of 4D MRI and CFD is crucial for accurate and efficient in-silico cardiovascular flow predictions for patient-specific geometries. Multiple CFD software, such as ANSYS, COMSOL, and SimVascular, have been widely used in published research works, yet the graphical user interfaces of these software packages have no explicit provisions for leveraging spatio-temporal 4D MRI data. Here, we present a framework to create accurate patient-specific CFD simulations leveraging spatiotemporal 4D MRI data and patient monitoring data. We discuss all aspects of patient-specific modeling, including geometry reconstruction, meshing, numerical simulation, and post-processing of results, focusing on the suitability of each software package and the ease with which the presented workflow can be implemented with the software.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".