Volume CT with a flat‐panel detector on a mobile, isocentric C‐arm: Pre‐clinical investigation in guidance of minimally invasive surgery
Bibliographic record
Abstract
A mobile isocentric C-arm (Siemens PowerMobil) has been modified in our laboratory to include a large area flat-panel detector (in place of the x-ray image intensifier), providing multi-mode fluoroscopy and cone-beam computed tomography (CT) imaging capability. This platform represents a promising technology for minimally invasive, image-guided surgical procedures where precision in the placement of interventional tools with respect to bony and soft-tissue structures is critical. The image quality and performance in surgical guidance was investigated in pre-clinical evaluation in image-guided spinal surgery. The control, acquisition, and reconstruction system are described. The reproducibility of geometric calibration, essential to achieving high three-dimensional (3D) image quality, is tested over extended time scales (7 months) and across a broad range in C-arm angulation (up to 45 degrees), quantifying the effect of improper calibration on spatial resolution, soft-tissue visibility, and image artifacts. Phantom studies were performed to investigate the precision of 3D localization (viz., fiber optic probes within a vertebral body) and effect of lateral projection truncation (limited field of view) on soft-tissue detectability in image reconstructions. Pre-clinical investigation was undertaken in a specific spinal procedure (photodynamic therapy of spinal metastases) in five animal subjects (pigs). In each procedure, placement of fiber optic catheters in two vertebrae (L1 and L2) was guided by fluoroscopy and cone-beam CT. Experience across five procedures is reported, focusing on 3D image quality, the effects of respiratory motion, limited field of view, reconstruction filter, and imaging dose. Overall, the intraoperative cone-beam CT images were sufficient for guidance of needles and catheters with respect to bony anatomy and improved surgical performance and confidence through 3D visualization and verification of transpedicular trajectories and tool placement. Future investigation includes improvement in image quality, particularly regarding x-ray scatter, motion artifacts and field of view, and integration with optical tracking and navigation systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".