Intraoperative ultrasound for guidance and tissue shift correction in image‐guided neurosurgery
Bibliographic record
Abstract
We present a surgical guidance system that incorporates pre-operative image information (e.g., MRI) with intraoperative ultrasound (US) imaging to detect and correct for brain tissue deformation during image-guided neurosurgery (IGNS). Many interactive IGNS implementations employ pre-operative images as a guide to the surgeons throughout the procedure. However, when a craniotomy is involved, tissue movement during a procedure can be a significant source of error in these systems. By incorporating intraoperative US imaging, the target volume can be scanned at any time, and two-dimensional US images may be compared directly to the corresponding slice from the pre-operative image. Homologous points may be mapped from the intraoperative to the pre-operative image space with an accuracy of better than 2 mm, enabling the surgeon to use this information to assess the accuracy of the guidance system along with the progress of the procedure (e.g., extent of lesion removal) at any time during the operation. Anatomical features may be identified on both the pre-operative and intraoperative images and used to generate a deformation map, which can be used to warp the pre-operative image to match the intraoperative US image. System validation is achieved using a deformable multi-modality imaging phantom, and preliminary clinical results are presented.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".