Magnetic Resonance Imaging-guided Neurosurgery in the Magnetic Fringe Fields: The Next Step in Neuronavigation
Bibliographic record
Abstract
OBJECTIVE: We describe the development of an alternative approach to intraoperative magnetic resonance imaging (iMR)-guided neurosurgery and report our initial experience with 22 craniotomies and 16 brain biopsies. The advantages and disadvantages of each approach are examined. METHODS: An iMR suite houses a 0.2-T open configuration system (Siemens Medical Systems, Erlangen, Germany) and is equipped with anesthetic gases and a magnetic resonance imaging (MRI)-compatible anesthesia machine and monitor. Standard operating instruments and equipment were tested for safety and compatibility in the magnetic fringe fields surrounding the open MRI system. We then performed brain biopsies and craniotomies in the iMR suite. RESULTS: Standard operating equipment functioned properly in the 0.5- to 10-mT zone and was not affected by the magnet's attractive force. Twenty-two craniotomies and 16 brain biopsies were performed in the interventional suite, using serial intraoperative MRI guidance, without injury to patients or operating room staff. CONCLUSION: Full neurosurgical procedures may be performed in the weak fringe fields surrounding an MRI system, using standard operating room equipment. This approach to iMR-guided neurosurgery offers a significant cost advantage over retrofitting an entire operative suite with "MRI-compatible" surgical equipment. The surgeon's familiarity with standard equipment and the reliability of the equipment are additional advantages. Neurosurgery in the fringe fields allows the neurosurgeon to utilize serial MRI with a minimum of inconvenience, disruption, and change to the standard neurosurgical procedure. Serial intraoperative imaging to visualize the changes in the brain that are associated with neurosurgical intervention seems to enhance the ability to safely and effectively accomplish neurosurgical goals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".