A 3D MRI sequence for computer assisted surgery of the lumbar spine
Why this work is in the frame
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Bibliographic record
Abstract
The aim of this research was to develop a magnetic resonance (MR) sequence capable of producing images suitable for use with computer assisted surgery (CAS) of the lumbar spine. These images needed good tissue contrast between bone and soft tissue to allow for image segmentation and generation of a 3D-surface model of the bone for surface registration. A 3D double echo fast gradient echo sequence was designed. Images were filtered for noise and non-uniformity and combined into a single data set. Registration experiments were carried out to directly compare segmentation of MR and computed tomography (CT) images using a physical model of a spine. These experiments showed the MR data produced adequate surface registration in 90% of the experiments compared to 100% with CT data. The MR images acquired using the sequence and processing described in this article are suitable to be used with CAS of the spine.
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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