Intraoperative ultrasound for guidance and tissue shift correction in image‐guided neurosurgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it