Integration and evaluation of a needle-positioning robot with volumetric microcomputed tomography image guidance for small animal stereotactic interventions
Bibliographic record
Abstract
PURPOSE: Preclinical research protocols often require insertion of needles to specific targets within small animal brains. To target biologically relevant locations in rodent brains more effectively, a robotic device has been developed that is capable of positioning a needle along oblique trajectories through a single burr hole in the skull under volumetric microcomputed tomography (micro-CT) guidance. METHODS: An x-ray compatible stereotactic frame secures the head throughout the procedure using a bite bar, nose clamp, and ear bars. CT-to-robot registration enables structures identified in the image to be mapped to physical coordinates in the brain. Registration is accomplished by injecting a barium sulfate contrast agent as the robot withdraws the needle from predefined points in a phantom. Registration accuracy is affected by the robot-positioning error and is assessed by measuring the surface registration error for the fiducial and target needle tracks (FRE and TRE). This system was demonstrated in situ by injecting 200 microm tungsten beads into rat brains along oblique trajectories through a single burr hole on the top of the skull under micro-CT image guidance. Postintervention micro-CT images of each skull were registered with preintervention high-field magnetic resonance images of the brain to infer the anatomical locations of the beads. RESULTS: Registration using four fiducial needle tracks and one target track produced a FRE and a TRE of 96 and 210 microm, respectively. Evaluation with tissue-mimicking gelatin phantoms showed that locations could be targeted with a mean error of 154 +/- 113 microm. CONCLUSIONS: The integration of a robotic needle-positioning device with volumetric micro-CT image guidance should increase the accuracy and reduce the invasiveness of stereotactic needle interventions in small animals.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".