Multimodality vascular imaging phantoms: A new material for the fabrication of realistic 3D vessel geometries
Bibliographic record
Abstract
Multimodality vascular flow phantoms provide a way of testing the geometric accuracy of clinical scanners and optimizing acquisition protocols with easy reproducibility of experimental conditions. This article presents a stereolithography method combined with a lost-material casting technique that eliminates metal residues of cerrolow (a low temperature melting point metallic alloy) left within irregular vessel lumens after casting. These residues potentially cause image artifacts especially in magnetic resonance angiography or flow disturbance. Geometrical accuracies of constructed lumens with isomalt, the proposed material, ranged from 3.3% to 5.7% for vessel diameters of 1.8-7.9 mm, which are comparable to those of lumens constructed with cerrolow that had better accuracies varying from 1.1% to 4.1% (p<0.02). Examples of geometries mimicking diseased arteries such as an aorta with stenosed renal arteries and an iliac artery with multiple stenoses are presented. This sugar-based isomalt material, combined with phantom designs having fiducial markers visible in digital subtraction angiography, computed tomography angiography, magnetic resonance angiography, and ultrasound [Med. Phys. 31, 1424-1433 (2004)], makes easier the fabrication of complex realistic and accurate replicas of pathological vessels with lumen irregularities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".