Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations
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Bibliographic record
Abstract
OBJECTIVE: Movements of brain tissue during neurosurgical procedures reduce the effectiveness of using pre-operative images for intra-operative surgical guidance. In this paper, we explore the use of acquiring intra-operative ultrasound (US) images for the quantification of and correction for non-linear brain deformations. MATERIALS AND METHODS: We will present a multi-modal registration strategy that automatically matches pre-operative images (e.g., MRI) to intra-operative US to correct for these deformations. The strategy involves using the predicted appearance of neuroanatomical structures in US images to build "pseudo ultrasound" images based on pre-operative segmented MRI. These images can then be non-linearly registered to intra-operative US using cross-correlation measurements within the ANIMAL package. The feasibility of the theory is demonstrated through its application to clinical patient data acquired during 12 neurosurgical procedures. RESULTS: Results of applying the method to 12 surgical cases, including those with brain tumors and selective amygdalo-hippocampectomies, indicate that our strategy significantly recovers from non-linear brain deformations occurring during surgery. Quantitative results at tumor boundaries indicate up to 87% correction for brain shift. CONCLUSIONS: Qualitative and quantitative examination of the results indicate that the system is able to correct for non-linear brain deformations in clinical patient data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it