Design and testing of an MR-conditional six-degree-of-freedom phantom robot
Bibliographic record
Abstract
OBJECTIVE: Motion phantoms can help accelerate and reduce the associated costs of research focused on motion-robust imaging. Currently available phantom robots for magnetic resonance imaging (MRI) lack sufficient degrees of freedom (DOF) to replicate complex physiological motions. This work presents the design and testing of a six-DOF MRI-conditional phantom robot to simulate such motions. Approach: The system was fabricated predominantly with 3D printed components as well as DC stepper motors. Testing validated the actuator's functionality and conditionality with a 3T MRI system. A Faraday cage to house the motors and electronics was constructed using a conductive coating on a 3D-printed shell. Main Results: The Faraday cage was found to reduce the noise power produced by the motors to the baseline level measured in the MRI without the robot being present within the MRI suite. A positional accuracy measured using a modified version of ISO 9283 was found to be 0.2mm and a rotational accuracy of [-0.1°, 0.3°, -0.2°] were measured for the x, y, and z directions, respectively. Path accuracy for sample motions was found to have a positional accuracy of 0.3 mm and rotational accuracy of [0.1°, 0.1°, 0.1°]. Significance: The created six-DOF robot enhances the development and validation of motion-robust imaging in MRI. The presented design is covered by WO patent #2023/184043, 2023/09/28. .
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".