Three‐dimensional tomosynthesis and cone‐beam computed tomography: An experimental study for fast, low‐dose intraoperative imaging technology for guidance of sinus and skull base surgery
Bibliographic record
Abstract
OBJECTIVES: To describe three-dimension (3-D) tomosynthesis and cone beam computed tomography (CBCT) as an intraoperative imaging system to guide both sinus and skull-base surgery in a cadaveric model. METHODS: Five cadaveric heads underwent baseline CBCT imaging. Surgical targets were resected from each head (uncinectomy, ethmoidectomy, medial maxillectomy, pituitary gland resection, and clivus ablation). Intraoperative imaging was provided so that for a given task, the acquisition arc (theta(tot) = 20 degrees , 45 degrees , 60 degrees , 90 degrees , 178 degrees ) of the tomosynthesis scan was fixed. Different heads and tasks were allocated different acquisition angles. There was no limit to the number of scans that could be requested. Residual target was highlighted with 3-D visualization software. RESULTS: The larger the image acquisition angle, the better the image. Only CBCT (theta(tot) approximately 178 degrees ) provided nearly isotropic 3-D spatial resolution and soft-tissue visibility in all three views. The volume of residual tissue remaining and the volume of adjacent-normal tissue that was resected were calculated as a function of tomosynthesis angle. For the easier surgical tasks (uncinectomy, ethmoidectomy) the residual tissue was not related to the tomosynthesis angle. However, for the difficult ablative tasks, the image quality became more important and tomosynthesis angle was related to the residual tissue. CONCLUSIONS: We describe an intraoperative imaging platform that can deliver near-real-time images of the target and related structures with low radiation dose. Tomosynthesis scanning angles higher than 60 degrees provided quantifiable benefits to the surgeon and facilitated total target ablation while helping to spare surrounding structures.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".