High-Resolution O-Arm Data Reconstruction for Optimized Intraoperative Imaging of Deep Brain Stimulation Leads: A Preclinical Study
Bibliographic record
Abstract
BACKGROUND: The employment of the O-arm for intraoperative localization of deep brain stimulation (DBS) leads has been shown to be feasible and effective. However, partial volume artifacts impede the determination of individual electrode contacts and thus allow only an indirect approximation of each contact's localization. OBJECTIVE: To reduce the partial volume artifacts by means of high-resolution (HiRes) reconstruction of O-arm data and thus allow more accurate predictions with regard to the positioning and orientation of individual DBS contacts. METHODS: Following intraoperative flat-panel computed tomography, the O-arm raw data were reconstructed with a resolution of 0.2 mm × 0.2 mm × 0.2 mm. The geometric integrity of HiRes reconstructions was assessed via landmark transformation. Using a phantom, resolutions of both reconstruction modalities were then evaluated by means of the modulation transfer function (MTF). Finally, directional and nondirectional leads were compared visually to analyze the delineation of individual electrode contacts. RESULTS: With a mean accuracy of 0.56 mm ± 0.12 mm, geometric integrity remained intact during HiRes reconstruction. Analysis of HiRes reconstruction resolution yielded a 47.7% increase of the 10% MTF in comparison to conventional postprocessing. Reduction of partial volume artifacts yielded strong contrasts of electrode compartments and allowed direct identification of individual contacts as well as localization of the X-ray marker on directional leads. CONCLUSION: HiRes reconstruction of O-arm data allows an effective reduction of partial volume artifacts to such an extent that a delineation of individual contacts across single DBS leads is possible without requiring increases in radiation dose.
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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 | 0.000 |
| 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) | 0.001 | 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 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".