Intraoperative cone-beam CT for image-guided tibial plateau fracture reduction
Bibliographic record
Abstract
Objectives: A mobile isocentric C-arm was modified in our laboratory in collaboration with Siemens Medical Solutions to include a large-area flat-panel detector providing multi-mode fluoroscopy and cone-beam CT (CBCT) imaging. This technology is an important advance over existing intraoperative imaging (e.g., Iso-C3D), offering superior image quality, increased field of view, higher spatial resolution, and soft-tissue visibility. The aim of this study was to assess the system's performance and image quality in tibial plateau (TP) fracture reconstruction.Methods: Three TP fractures were simulated in fresh-frozen cadaveric knees through combined axial loading and lateral impact. The fractures were reduced through a lateral approach and assessed by fluoroscopy. The reconstruction was then assessed using CBCT. If necessary, further reduction and localization of remaining displaced bone fragments was performed using CBCT images for guidance. CBCT image quality was assessed with respect to projection speed, dose and filtering technique.Results: CBCT imaging provided exquisite visualization of articular details, subtle fragment detection and localization, and confirmation of reduction and implant placement. After fluoroscopic images indicated successful initial reduction, CBCT imaging revealed areas of malalignment and displaced fragments. CBCT facilitated fragment localization and improved anatomic reduction. CBCT image noise increased gradually with reduced dose, but little difference in images resulted from increased projections. High-resolution reconstruction provided better delineation of plateau depressions.Conclusion: This study demonstrated a clear advantage of intraoperative CBCT over 2D fluoroscopy and Iso-C3D in TP fracture fixation. CBCT imaging provided benefits in fracture type diagnosis, localization of fracture fragments, and intraoperative 3D confirmation of anatomic reduction.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".