Accuracy study of a robotic system for MRI‐guided prostate needle placement
Bibliographic record
Abstract
BACKGROUND: Accurate needle placement is the first concern in percutaneous MRI-guided prostate interventions. In this phantom study, different sources contributing to the overall needle placement error of a MRI-guided robot for prostate biopsy have been identified, quantified and minimized to the possible extent. METHODS: The overall needle placement error of the system was evaluated in a prostate phantom. This error was broken into two parts: the error associated with the robotic system (called 'before-insertion error') and the error associated with needle-tissue interaction (called 'due-to-insertion error'). Before-insertion error was measured directly in a soft phantom and different sources contributing into this part were identified and quantified. A calibration methodology was developed to minimize the 4-DOF manipulator's error. The due-to-insertion error was indirectly approximated by comparing the overall error and the before-insertion error. The effect of sterilization on the manipulator's accuracy and repeatability was also studied. RESULTS: The average overall system error in the phantom study was 2.5 mm (STD = 1.1 mm). The average robotic system error in the Super Soft plastic phantom was 1.3 mm (STD = 0.7 mm). Assuming orthogonal error components, the needle-tissue interaction error was found to be approximately 2.13 mm, thus making a larger contribution to the overall error. The average susceptibility artifact shift was 0.2 mm. The manipulator's targeting accuracy was 0.71 mm (STD = 0.21 mm) after robot calibration. The robot's repeatability was 0.13 mm. Sterilization had no noticeable influence on the robot's accuracy and repeatability. CONCLUSIONS: The experimental methodology presented in this paper may help researchers to identify, quantify and minimize different sources contributing into the overall needle placement error of an MRI-guided robotic system for prostate needle placement. In the robotic system analysed here, the overall error of the studied system remained within the acceptable range.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".