Technical Note: Unsupervised C‐arm pose tracking with radiographic fiducial
Bibliographic record
Abstract
PURPOSE: C-arm fluoroscopy reconstruction, such as that used in prostate brachytherapy, requires that the relative poses of the individual C-arm fluoroscopy images must be known prior to reconstruction. Radiographic fiducials can provide excellent C-arm pose tracking, but they need to be segmented in the image. The authors report an automated and unsupervised method that does not require prior segmentation of the fiducial. METHODS: The authors compute the individual C-arm poses relative to a stationary radiographic fiducial of known geometry. The authors register a filtered 2D fluoroscopy image of the fiducial to its 3D model by using image intensity alone without prior segmentation. To enhance the C-arm images, the authors investigated a three-step cascade filter and a line enhancement filter. The authors tested the method on a composite fiducial containing beads, straight lines, and ellipses. Ground-truth C-arm pose was provided by a clinically proven method. RESULTS: Using 111 clinical C-arm images and +/- 10 degrees and +/- 10 mm random perturbation around the ground-truth pose, a total of 2775 cases were evaluated. The average rotation and translation errors were 0.62 degrees (STD = 0.31 degrees) and 0.72 mm (STD = 0.55 mm) for the three-step filter and 0.67 degrees (STD = 0.40 degrees) and 0.87 mm (STD = 0.27 mm) using the line enhancement filter. CONCLUSIONS: The C-arm pose tracking method was sufficiently accurate and robust on human patient data for subsequent 3D implant reconstruction.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".