Advances in Mobile Intraoperative Magnetic Resonance Imaging
Bibliographic record
Abstract
OBJECTIVE: The goal was to enhance a mobile magnetic resonance imaging system developed for neurosurgery. Components of the system included an actively shielded, 1.5-T superconducting magnet, a titanium operating room table, a radiofrequency (RF) head coil that could be disassembled, and local RF shielding. METHODS: The system was designed and implemented by the Division of Neurosurgery, University of Calgary (Calgary, Alberta, Canada), in collaboration with the National Research Council of Canada Institute for Biodiagnostics (Winnipeg, Manitoba, Canada). The ceiling-mounted, 1.5-T magnet was moved into and out of the surgical field as required. After initial success in monitoring the resection of various intracranial and cranial base lesions, significant modifications to the system were made by Innovative Magnetic Resonance Imaging Systems, Inc. (Winnipeg, Manitoba, Canada), and BrainLAB (Heimstetten, Germany). These modifications included the design and construction of a shorter magnet with a larger bore and stronger gradients, widening of the titanium operating room table, modification of the RF coil housing to allow vertical movement and incorporation of a three-pin head-clamp, construction of a transparent, copper-impregnated RF shield, and integration with a surgical navigation system. RESULTS: The movable intraoperative imaging system has now been used for 101 neurosurgical procedures, including the previously reported cases. CONCLUSION: The modifications to the system have enhanced its integration with established neurosurgical techniques and have improved patient safety. The larger magnet bore size, together with the ability to move the RF coil vertically, allows placement of patients in prone or lateral positions. Surgical navigation has been successfully integrated with the intraoperatively acquired high-resolution images. The ability to identify and resect residual lesions before wound closure remains a tremendous immediate advantage of this technology.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 0.002 |
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".