3D image‐guided robotic needle positioning system for small animal interventions
Bibliographic record
Abstract
PURPOSE: This paper presents the design of a micro-CT guided small animal robotic needle positioning system. In order to simplify the robotic design and maintain a small targeting error, a novel implementation of the remote center of motion is used in the system. The system has been developed with the objective of achieving a mean targeting error of <200 μm while maintaining a high degree of user friendliness. METHODS: The robot is compact enough to operate within a 25 cm diameter micro-CT bore. Small animals can be imaged and an intervention performed without the need to transport the animal from one workspace to another. Not requiring transport of the animal reduces opportunities for targets to shift from their localized position in the image and simplifies the workflow of interventions. An improved method of needle calibration is presented that better characterizes the calibration using the position of the needle tip in photographs rather than the needle axis. A calibration fixture was also introduced, which dramatically reduces the time requirements of calibration while maintaining calibration accuracy. Two registration modes have been developed to correspond the robot coordinate system with the coordinate system of the micro-CT scanner. The two registration modes offer a balance between the time required to complete a registration and the overall registration accuracy. The development of slow high accuracy and fast low accuracy registration modes provides users with a degree of flexibility in selecting a registration mode best suited for their application. RESULTS: The target registration error (TRE) of the higher accuracy primary registration was TRE(primary) = 31 ± 12 μm. The error in the lower accuracy combined registration was TRE(combined) = 139 ± 63 μm. Both registration modes are therefore suitable for small-animal needle interventions. The targeting accuracy of the robotic system was characterized using targeting experiments in tissue-mimicking gelatin phantoms. The results of the targeting experiments were combined with the known calibration and needle deflection errors to provide a more meaningful measure of the needle positioning accuracy of the system. The combined targeting errors of the system were 149 ± 41 μm and 218 ± 38 μm using the primary and combined registrations, respectively. Finally, pilot in vivo experiments were successfully completed to demonstrate the performance of the system in a biomedical application. CONCLUSIONS: The device was able to achieve the desired performance with an error of <200 μm and improved repeatability when compared to other designs. The device expands the capabilities of image-guided interventions for preclinical biomedical applications.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".