Validation of 3D reconstructions of a mimicked femoral artery with an ultrasound imaging robotic system
Bibliographic record
Abstract
PURPOSE: The degree of stenosis is the most important criterion to assess peripheral arterial disease manifested by atherosclerosis mainly in lower limb arteries. Ultrasound (U.S.) imaging offers low-cost, safe, and convenient options to evaluate this disease, but most U.S. freehand approaches cannot optimally locate stenoses and map lower limb arterial geometries. A 3D-U.S. imaging robotic system that can control and standardize image acquisition by scanning typically encountered diseased arterial lower limb segments is presented and validated with phantoms. METHODS: A Z-phantom calibration procedure was used to characterize spatial transformation of the U.S. probe image plane for different clinical image acquisition settings. Moreover, the accuracy of the calibration transform to reconstruct a lower-limb-mimicking vessel geometry was evaluated with a vascular phantom. RESULTS: A 3D calibration precision of 0.47 +/- 0.27 mm was achieved. Reconstruction errors were less than 1.74 +/- 0.08 mm in all 3D vessel representations and the cross-sectional areas of each image section were close to those of gold standard phantom measures. The best reconstruction accuracy (smallest error) was 0.40 +/- 0.03 mm. CONCLUSION: Altogether, these results demonstrate the potential of the robotic scanner to adequately represent lower limb vessels for the clinical evaluation of stenoses.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".