Effect of fiducial configuration on target registration error in intraoperative cone-beam CT guidance of head and neck surgery
Bibliographic record
Abstract
Advances in image-guided surgery have led to minimally-invasive, high-precision procedures that increase the efficacy of treatment, minimize surgical complications, and reduce patient recovery time. A recent advance in intraoperative 3D imaging includes cone-beam CT (CBCT) implemented on a mobile C-arm. This paper investigates the effect of the number and configuration of fiducials on target registration error (TRE) and identifies fiducial configurations that minimize TRE for rigid point-based registration in CBCT-guided head and neck surgery. Best configurations were those that minimized the distance between the centroid of fiducials and the surgical target while maximizing fiducial separation (distance from principal axes). Configurations with as few as 4 fiducials could be identified that minimized TRE (e.g., TRE < 0.3 mm for the pituitary, cochlea, and nasion), with more fiducials (6 or more) providing improved TRE uniformity throughout the volume of clinical interest. If possible, fiducials affixed to the skin or cranium (e.g., 4-6 markers) should include a majority about the target (to minimize centroid-to-target distance) with others at a distance (to maximize separation). A greater number of fiducials distributed evenly can provide low, uniform TRE for all targets--e.g., 8 markers, TRE approximately 0.2-0.6 mm throughout the volume of interest. Such work helps guide the implementation of C-arm CBCT in head and neck surgery in a manner that maximizes surgical precision and exploits intraoperative image guidance to its full potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".