Angiography Simulation and Planning Using a Multi-Fluid Approach
Bibliographic record
Abstract
Angiography is a minimally invasive diagnostic procedure in endovascular interventions. Training interventional procedures is a big challenge, due to the complexity of the procedures with the changes of measurement and visualization in blood flow rate, volume, and image contrast. In this paper, we present a novel virtual reality-based 3D interactive training platform for angiography procedure training. We propose a multi-fluid flow approach with a novel corresponding non-slip boundary condition to simulate the effect of diffusion between the blood and contrast media. A novel syringe device tool is also designed as an add-on hardware to the 3D software simulation system to model haptics through real physical interactions to enhance the realism of the simulation-based training. Experimental results show that the system can simulate realistic blood flow in complex blood vessel structures. The results are validated by visual comparisons between real angiography images and simulations. By combining the proposed software and hardware, our system is applicable and scalable to many interventional radiology procedures. Finally, we have tested the system with clinicians to assess its efficacy for virtual reality-based medical training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".