Computational replicas: anatomic reconstructions of cerebral vessels as volume numerical grids at three-dimensional angiography.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We present a relatively simple approach that physicians can use to reconstruct cerebral vessels as 3D numerical grids or computational replicas. The method accurately duplicates their geometry to provide computer simulations of their blood flow. METHODS: Initial images were obtained by using any medical imaging technique, such as MR angiography, CT angiography, or 3D digital subtraction angiography. The data were collected in DICOM format and converted by a DICOM reader into a 3D gray-scale raster image. The image was then processed by using commercial visualization and mesh generation software, which allowed extraction of the luminal surface of the blood vessel (by using the isosurfacing technique). The subsequent final output was an unstructured tetrahedral grid that can be directly used for detailed analysis of cerebral vascular geometry for patient-specific simulations of blood flow. RESULTS: Four examples of grid reconstruction and blood flow simulation for patients with ruptured aneurysms were validated with angiographic and operative findings. The ruptured areas were correlated with areas of high fluid-induced wall-shear stress. CONCLUSION: This approach promises to be a practical tool for planning treatment and follow-up of patients after neurosurgical or endovascular interventions with 3D angiography. The proposed commercial packages or conceptually similar ones seem to be relatively simple and suitable for direct use by neurosurgeons or neuroradiologists.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".