Visualization of Brain Shift Corrected Functional Magnetic Resonance Imaging Data for Intraoperative Brain Mapping
Bibliographic record
Abstract
BACKGROUND: Brain tumor surgery requires careful balance between maximizing tumor excision and preserving eloquent cortex. In some cases, the surgeon may opt to perform an awake craniotomy including intraoperative mapping of brain function by direct cortical stimulation (DCS) to assist in surgical decision-making. Preoperatively, functional magnetic resonance imaging (fMRI) facilitates planning by identification of eloquent brain areas, helping to guide DCS and other aspects of the surgical plan. However, brain deformation (shift) limits the usefulness of preoperative fMRI during surgery. To address this, an integrated visualization method for fMRI and DCS results is developed that is intuitive for the surgeon. METHODS: An image registration pipeline was constructed to display preoperative fMRI data corrected for brain shift overlaid on images of the exposed cortical surface at the beginning and completion of DCS mapping. Preoperative fMRI and DCS data were registered for a range of misalignments, and the residual registration errors were calculated. The pipeline was validated on imaging data from five brain tumor patients who underwent awake craniotomy. RESULTS: Registration errors were well under 5 mm (the approximate spatial resolution of DCS) for misalignments of up to 25 mm and approximately 10-15°. For rotational misalignments up to 20°, the success rate was 95% for an error tolerance of 5 mm. Failures were negligible for rotational misalignments up to 10°. Good quality registrations were observed for all five patients. CONCLUSIONS: A proof-of-concept image registration pipeline is presented with acceptable accuracy for intraoperative use, providing multimodality visualization with potential benefits for intraoperative brain mapping.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".