Multimode C-arm fluoroscopy, tomosynthesis, and cone-beam CT for image-guided interventions: from proof of principle to patient protocols
Bibliographic record
Abstract
High-performance intraoperative imaging is essential to an ever-expanding scope of therapeutic procedures ranging from tumor surgery to interventional radiology. The need for precise visualization of bony and soft-tissue structures with minimal obstruction to the therapy setup presents challenges and opportunities in the development of novel imaging technologies specifically for image-guided procedures. Over the past ~5 years, a mobile C-arm has been modified in collaboration with Siemens Medical Solutions for 3D imaging. Based upon a Siemens PowerMobil, the device includes: a flat-panel detector (Varian PaxScan 4030CB); a motorized orbit; a system for geometric calibration; integration with real-time tracking and navigation (NDI Polaris); and a computer control system for multi-mode fluoroscopy, tomosynthesis, and cone-beam CT. Investigation of 3D imaging performance (noise-equivalent quanta), image quality (human observer studies), and image artifacts (scatter, truncation, and cone-beam artifacts) has driven the development of imaging techniques appropriate to a host of image-guided interventions. Multi-mode functionality presents a valuable spectrum of acquisition techniques: i.) fluoroscopy for real-time 2D guidance; ii.) limited-angle tomosynthesis for fast 3D imaging (e.g., ~10 sec acquisition of coronal slices containing the surgical target); and iii.) fully 3D cone-beam CT (e.g., ~30-60 sec acquisition providing bony and soft-tissue visualization across the field of view). Phantom and cadaver studies clearly indicate the potential for improved surgical performance - up to a factor of 2 increase in challenging surgical target excisions. The C-arm system is currently being deployed in patient protocols ranging from brachytherapy to chest, breast, spine, and head and neck surgery.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".