A Moveable 3-Tesla Intraoperative Magnetic Resonance Imaging System
Bibliographic record
Abstract
BACKGROUND: Based on success with a prototype 1.5T intraoperative magnetic resonance imaging (iMRI) system and the desire for increased signal-to-noise ratio, along with its relationship to image quality and advanced applications, a 3.0T system that uses the same novel moveable magnet configuration was developed. OBJECTIVE: To assess clinical applicability by prospectively applying the higher-field system to a neurosurgical cohort. METHODS: Upgrading to 3.0T required substantial modification of an existing iMRI-equipped operating room. The 1.5T magnet was replaced with a ceiling-mounted, moveable 3.0T magnet with a 70-cm working aperture. Local radiofrequency shielding was replaced with whole-room shielding. A new hydraulic operating table, high-performance gradients, and advanced image processing software were also installed. The new system was used as an adjunct to standard neurosurgical practice. RESULTS: The iMRI system upgrade required 6 months. Since completion, the 3.0T iMRI system has successfully guided neurosurgery in 120 patients without system failure in a patient-focused environment. Intraoperative image quality was superior to that obtained at 1.5T and enabled intraoperative acquisition of advanced imaging sequences, including tractography. Intraoperative imaging was found to modify surgery in a substantial number of patients. CONCLUSION: Implementation of an iMRI system based on a moveable 3.0T magnet is feasible. From clinical experience with 120 patients, iMRI at 3.0T is safe, reliable, and capable of directing image-guided surgery with exceptional image quality.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".