Design and validation of an MR‐conditional robot for transcranial focused ultrasound surgery in infants
Bibliographic record
Abstract
PURPOSE: Current treatment of intraventricular hemorrhage (IVH) involves cerebral shunt placement or an invasive brain surgery. Magnetic resonance-guided focused ultrasound (MRgFUS) applied to the brains of pediatric patients presents an opportunity to treat IVH in a noninvasive manner, termed "incision-less surgery." Current clinical and research focused ultrasound systems lack the capability to perform neonatal transcranial surgeries due to either range of motion or dexterity requirements. A novel robotic system is proposed to position a focused ultrasound transducer accurately above the head of a neonatal patient inside an MRI machine to deliver the therapy. METHODS: A clinical Philips Sonalleve MRgFUS system was expanded to perform transcranial treatment. A five degree-of-freedom MR-conditional robot was designed and manufactured using MR compatible materials. The robot electronics and control were integrated into existing Philips electronics and software interfaces. The user commands the position of the robot with a graphical user interface, and is presented with real-time MR imaging of the patient throughout the surgery. The robot is validated through a series of experiments that characterize accuracy, signal-to-noise ratio degeneration of an MR image as a result of the robot, MR imaging artifacts generated by the robot, and the robot's ability to operate in a representative surgical environment inside an MR machine. RESULTS: Experimental results show the robot responds reliably within an MR environment, has achieved 0.59 ± 0.25 mm accuracy, does not produce severe MR-imaging artifacts, has a workspace providing sufficient coverage of a neonatal brain, and can manipulate a 5 kg payload. A full system demonstration shows these characteristics apply in an application environment. CONCLUSIONS: This paper presents a comprehensive look at the process of designing and validating a new robot from concept to implementation for use in an MR environment. An MR conditional robot has been designed and manufactured to design specifications. The system has demonstrated its feasibility as a platform for MRgFUS interventions for neonatal patients. The success of the system in experimental trials suggests that it is ready to be used for validation of the transcranial intervention in animal studies.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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".