Sino Canada Health Institute Intra-Operative MRI
Bibliographic record
Abstract
Accurately targeting specific regions of interest in the brain is pivotal for the success of neurosurgical procedures. For example, the outcome of brain tumor resection is improved dramatically when surgeons are better able to define surgical borders. Interventional MRI (iMRI) helps reduce the risk of damaging critical areas of the brain and makes it possible to confirm a successful resection or determine the need for further resection prior to closing a patients head and finalizing the surgery. The Sino Canada Health Institute (SCHI) is developing a small, lightweight movable system for performing intra-operative magnetic resonance imaging. The scanner will be based on a rampable magnet that can be energized and moved into place over the patient for surgical procedures. When not in use, the magnet will be discharged and stored locally in a small room. Moving the scanner will be facilitated by a track mounted crawler system allowing it to be transported and positioned as needed. The use of optical guidance will ensure precise, consistent placement of the scanner to within 1mm. This will be crucial when taking post-surgery images as consistent alignment with the pre-surgery images is important. A modular approach is being explored such that this technology can be integrated into existing hospitals around the world. Highly optimized rf coil arrays will be employed to help ensure imaging quality, and in addition, novel image reconstruction techniques will be used during post-processing. This will provide the opportunity to achieve high resolution images at relatively low field strengths (of order 1T). In this talk I will provide details about the design and development of the SCHI iMRI system. This will include technical specifications relating to the magnet mover, rf coil development, as well as provide the latest results from tests of the prototype system. The authors wish to acknowledge funding from NSERC partnership grants, and Mitacs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.099 | 0.043 |
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".