Registration of a needle-positioning robot to high-resolution 3D ultrasound and computed tomography for image-guided interventions in small animals
Bibliographic record
Abstract
Preclinical research often requires the delivery of biological substances to specific locations in small animals. Guiding a needle to targets in small animals with an error < 200 μm requires accurate registration. We are developing techniques to register a needle-positioning robot to high-resolution three-dimensional ultrasound and computed tomography small animal imaging systems. Both techniques involve moving the needle to predetermined robot coordinates and determining corresponding needle locations in image coordinates. Registration accuracy will therefore be affected by the robot positioning error and is assessed by measuring the target registration error (TRE). A point-based registration between robot and micro-ultrasound coordinates was accomplished by attaching a fiducial phantom onto the needle. A TRE of 145 μm was achieved when moving the needle to a set of robot coordinates and registering the coordinates to needle tip locations determined from ultrasound fiducial measurements. Registration between robot and micro-CT coordinates was accomplished by injecting barium sulfate into tracks created when the robot withdraws the needle from a phantom. Points along cross-sectional slices of the segmented needle tracks were determined using an intensity-weighted centroiding algorithm. A minimum distance TRE of 194 ± 18 μm was achieved by registering centroid points to robot trajectories using the iterative closest point (ICP) algorithm. Simulations, incorporating both robot and ultrasound fiducial localization errors, verify that robot error is a significant component of the experimental registration. Simulations of micro-CT to robot ICP registration similarly agree with the experimental results. Both registration techniques produce a TRE < 200 μm, meeting design specification.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".