3D-printed phantom for the characterization of non-uniform rotational distortion(Conference Presentation)
Bibliographic record
Abstract
Endoscopic catheter-based imaging systems that employ a 2-dimensional rotary or 3-dimensional rotary-pullback scanning mechanism require constant angular velocity at the distal tip to ensure correct angular registration of the collected signal. Non-uniform rotational distortion (NURD) – often present due to a variety of mechanical issues – can result in inconsistent position and velocity profiles at the tip, limiting the accuracy of any measurements. Since artifacts like NURD are difficult to identify and characterize during tissue imaging, phantoms with well-defined patterns have been used to quantify position and/or velocity error. In this work we present a fast, versatile, and cost-effective method for making fused deposition modeling 3D printed phantoms for identifying and quantifying NURD errors along an arbitrary user-defined pullback path. Eight evenly-spaced features are present at the same orientation at all points on the path such that deviations from expected geometry can be quantified for the imaging catheter. The features are printed vertically and then folded together around the path to avoid issues with printer head resolution. This method can be adapted for probes of various diameters and for complex imaging paths with multiple bends. We demonstrate imaging using the 3D printed phantoms with a 1mm diameter rotary-pullback OCT catheter and system as a means of objectively evaluating the mechanical performance of similarly constructed probes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.009 | 0.003 |
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".