Magnetic resonance imaging properties of multimodality anthropomorphic silicone rubber phantoms for validating surgical robots and image guided therapy systems
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 T1 and T2 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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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) | 0.000 | 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) | 0.001 | 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".