WE‐C‐330A‐02: Cone‐Beam CT for Image‐Guided Head and Neck Surgery: Assessment of Dose and Image Quality Using a C‐Arm Prototype
Bibliographic record
Abstract
Purpose: To quantify radiation dose to patient and staff during intraoperative cone‐beam CT (CBCT)‐guided head and neck surgery, and investigate 3D imaging performance as a function of dose. Method and Materials: Dose and image quality measurements were acquired on a mobile C‐arm modified at our institution to provide flat‐panel CBCT. Imaging dose was measured in a custom‐built 16 cm cylindrical head phantom at four positions (isocenter, anterior, posterior, and lateral) as a function of kVp (80–120 kVp) and C‐arm trajectory (“tube‐under” and “tube‐over” 180° orbits). In‐room exposure was measured at positions around the operating table and up to 2 m from isocenter. 3D image quality was assessed in CBCT reconstructions of an anthropomorphic head phantom containing contrast‐detail spheres (11–103 HU; 1.6–12.7 mm). The contrast‐to‐noise ratio (CNR) was evaluated across a broad range of dose (0.6–23.3 mGy). Results: Dose in the 16 cm phantom (100 kVp; “tube‐under” orbit) was 0.059 (isocenter), 0.022 (anterior), 0.10 (posterior), and 0.056 (lateral) mGy/mAs. Dose to the eyes (anterior) was reduced by a factor of 10 for “tube‐under” versus “tube‐over” orbits. In‐room exposure for a typical CBCT scan (∼10 mGy to isocenter) ranged from 33 mR at the anesthetist position, to <0.5 mR at 2 m from isocenter. CNR increased as the square root of dose, with excellent visualization of bony and soft‐tissue structures in the anthropomorphic head phantom achieved at ∼3mGy and ∼10mGy, respectively. Conclusion: The prototype C‐arm CBCT system demonstrates excellent visualization of bony and soft‐tissue structures at dose levels low enough for repeat intraoperative imaging. High‐performance image‐guidance with respect to bony and soft‐tissue anatomy was achieved at doses <∼3mGy and <∼10mGy, respectively. For guidance of head and neck surgery, significant dose sparing to the patient's eyes (a factor of 10) is achieved using a “tube‐under” (rather than “tube‐over”) 180deg; orbit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.005 | 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".