High Speed, High Density Intraoperative 3D Optical Topographical Imaging with Efficient Registration to MRI and CT for Craniospinal Surgical Navigation
Bibliographic record
Abstract
Intraoperative image-guided surgical navigation for craniospinal procedures has significantly improved accuracy by providing an avenue for the surgeon to visualize underlying internal structures corresponding to the exposed surface anatomy. Despite the obvious benefits of surgical navigation, surgeon adoption remains relatively low due to long setup and registration times, steep learning curves, and workflow disruptions. We introduce an experimental navigation system utilizing optical topographical imaging (OTI) to acquire the 3D surface anatomy of the surgical cavity, enabling visualization of internal structures relative to exposed surface anatomy from registered preoperative images. Our OTI approach includes near instantaneous and accurate optical measurement of >250,000 surface points, computed at >52,000 points-per-second for considerably faster patient registration than commercially available benchmark systems without compromising spatial accuracy. Our experience of 171 human craniospinal surgical procedures, demonstrated significant workflow improvement (41 s vs. 258 s and 794 s, p < 0.05) relative to benchmark navigation systems without compromising surgical accuracy. Our advancements provide the cornerstone for widespread adoption of image guidance technologies for faster and safer surgeries without intraoperative CT or MRI scans. This work represents a major workflow improvement for navigated craniospinal procedures with possible extension to other image-guided applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".