Magnetic resonance imaging properties of multimodality anthropomorphic silicone rubber phantoms for validating surgical robots and image guided therapy systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of image guided robotic and mechatronic platforms for medical applications requires a phantom model for initial testing. Finding an appropriate phantom becomes challenging when the targeted patient population is pediatrics, particularly infants, neonates or fetuses. Our group is currently developing a pediatricsized surgical robot that operates under fused MRI and laparoscopic video guidance. To support this work, we describe a method for designing and manufacturing silicone rubber organ phantoms for the purpose of testing the robotics and the image fusion system. A surface model of the organ is obtained and converted into a mold that is then rapid-prototyped using a 3D printer. The mold is filled with a solution containing a particular ratio of silicone rubber to slacker additive to achieve a specific set of tactile and imaging characteristics in the phantom. The expected MRI relaxation times of different ratios of silicone rubber to slacker additive are experimentally quantified so that the imaging properties of the phantom can be matched to those of the organ that it represents. Samples of silicone rubber and slacker additive mixed in ratios ranging from 1:0 to 1:1.5 were prepared and scanned using inversion recovery and spin echo sequences with varying TI and TE, respectively, in order to fit curves to calculate the expected T<sub>1</sub> and T<sub>2</sub> relaxation times of each ratio. A set of infantsized abdominal organs was prepared, which were successfully sutured by the robot and imaged using different modalities.
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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 | 0.001 | 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 | 0.001 |
| 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 it