Application of T1 and T2 Maps for Stereotactic Deep-Brain Neurosurgery Planning
Bibliographic record
Abstract
This work presents the application of a quantitative magnetic resonance imaging (MRI) technique in stereotactic deep-brain neurosurgery planning procedures. The high spatial resolution T1and T2maps acquired using this imaging method have been normalized to the standard CJH-27 brain coordinate system and integrated into a neurosurgical visualization and navigation system to improve the accuracy of surgical target localization. The T1and T2maps, along with the standardized anatomical and functional information within this system, can be navigated, non-rigidly registered, and arbitrarily processed. Once applied to individual patients, these maps facilitate the delineation of surgical targets. Our preliminary studies compared the centroids of segmented deep-brain nuclei based on the T1and T2maps with those according to Schaltenbrand and Wahren atlas, and with the actual surgical targets of 15 patients who had undergone thalamotomy, pallidotomy, and subthalamic nucleus deep-brain stimulation. The average displacement was 3.21mm±0.80mm, indicating the potential capability of this system to accurately initiate target identifications.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".