Detection of flow dynamic changes in 3D printed aneurysm models after treatment
Bibliographic record
Abstract
Treatment success and potential relapse of intracranial aneurysms need to be followed-up by regular imaging. How- ever, the metallic material inside treated aneurysms can cause artifacts in MRI, CT and DSA possibly compromising clinical interpretation. Furthermore, frequent follow-ups with X-ray based imaging methods seriously increase the patient’s exposure to ionizing radiation. Thus, magnetic particle imaging (MPI) may be beneficial for patients with treated aneurysms. The purpose of this work was to demonstrate the capability of MPI to depict the change of the contrast agent dynamics of aneurysms after treatment. Realistic aneurysm models before and after treatment with different approaches were connected to a peristaltic pump with a physiologic flow (250 ml/min) and pulsation rate (70/min). Contrast agent curves over time were measured during injection of a 3 ml bolus within 3 s of an aqueous solution of 50 mmol(Fe)/L. MPI was able to detect the expected delay and dispersion of the contrast agent in the treated aneurysm as well as reduced filling with contrast agent, if densely packed material was present inside the aneurysm. The delay was estimated based on the MPI contrast agent curves to be in the order of about 1 s. Thus, MPI is capable to detect delay and dispersion of the contrast agent dynamics after aneurysm treatment with clinical standard metallic material.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".