Virtual angiography for visualization and validation of computational models of aneurysm hemodynamics
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Bibliographic record
Abstract
It has recently become possible to simulate aneurysmal blood flow dynamics in a patient-specific manner via the coupling of three-dimensional (3-D) X-ray angiography and cmputational fluid dynamics (CFD). Before such image-based CFD models can be used in a predictive capacity, however, it must be shown that they indeed reproduce the in vivo hemodynamic environment. Motivated by the fact that there are currently no techniques for adequately measuring complex blood velocity fields in vivo, in this paper we describe how cine X-ray angiograms may be simulated for the purpose of indirectly validating patient-sperific CFD models. Mimicking the radiological procedure, a virtual angiogram is constructed by first simulating the time-varying injection of contrast agent into a precomputed, patient-specific CFD model. A time-series of images is then constructed by simulating the attenuation of X-rays through the computed 3-D contrast-agent flow dynamics. Virtual angiographic images and residence time maps, here derived from an image-based CFD model of a giant aneurysm, are shown to be in excellent agreement wiith the corresponding clinical images and residence time maps, but only when the interaction between the quasisteady contrast agent injection and the pulsatile flow are properly accounted for. These virtual angiographic techniques pave the way for validating image-based CFD models against routinely available clinical data, and provide a means of visualizing complex, 3-D blood flow dynamics in a clinically relevant manner. They also clearly show how the contrast agent injection perturbs the noraml blood flow patterns, further highlighting the potential utility of image-based CFD as a window into the true aneurysmal hemodynamics.
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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 | 0.000 |
| 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