A structural approach to 3D-printing arterial phantoms with physiologically comparable mechanical characteristics: Preliminary observations
Bibliographic record
Abstract
Pulse wave behavior is important in cardiovascular pathophysiology and arterial phantoms are valuable for studying arterial function. The ability of phantoms to replicate complex arterial elasticity and anatomy is limited by available materials and techniques. The feasibility of improving phantom performance using functional structure designs producible with practical 3D printing technologies was investigated. A novel corrugated wall approach to separate phantom function from material properties was investigated with a series of designs printed from polyester-polyurethane using a low-cost open-source fused filament fabrication 3D printer. Nonpulsatile pressure-diameter data was collected, and a mock circulatory system was used to observe phantom pulse wave behavior and obtain pulse wave velocities. The measured range of nonpulsatile Peterson elastic strain modulus was 5.6–19 to 12.4–33.0 kPa over pressures of 5–35 mmHg for the most to least compliant designs respectively. Pulse wave velocities of 1.5–5 m s −1 over mean pressures of 7–55 mmHg were observed, comparing favorably to reported in vivo pulmonary artery measurements of 1–4 m s −1 across mammals. Phantoms stiffened with increasing pressure in a manner consistent with arteries, and phantom wall elasticity appeared to vary between designs. Using a functional structure approach, practical low-cost 3D-printed production of simple arterial phantoms with mechanical properties that closely match the pulmonary artery is possible. Further functional structure design development to expand the pressure range and physiologic utility of dir"ectly 3D-printed phantoms appears warranted.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".