A multimodality vascular imaging phantom with fiducial markers visible in DSA, CTA, MRA, and ultrasound
Bibliographic record
Abstract
The objective was to design a vascular phantom compatible with digital subtraction angiography, computerized tomography angiography, ultrasound and magnetic resonance angiography (MRA). Fiducial markers were implanted at precise known locations in the phantom to facilitate identification and orientation of plane views from three-dimensional (3-D) reconstructed images. A vascular conduit connected to tubing at the extremities of the phantom ran through an agar-based gel filling it. A vessel wall in latex was included around the conduit to avoid diffusion of contrast agents. Using a lost-material casting technique based on a low melting point metal, geometries of pathological vessels were modeled. During the experimental testing, fiducial markers were detectable in all modalities without distortion. No leak of gadolinium through the vascular wall was observed on MRA after 5 hours. Moreover, no significant deformation of the vascular conduit was noted during the fabrication process (confirmed by microtome slicing along the vessel). The potential use of the phantom for calibration, rescaling, and fusion of 3-D images obtained from the different modalities as well as its use for the evaluation of intra- and inter-modality comparative studies of imaging systems are discussed. In conclusion, the vascular phantom can allow accurate calibration of radiological imaging devices based on x-ray, magnetic resonance and ultrasound and quantitative comparisons of the geometric accuracy of the vessel lumen obtained with each of these methods on a given well defined 3-D geometry.
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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 | 0.001 | 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".