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Record W2476772630 · doi:10.1118/1.4955174

Design and validation of an MR‐conditional robot for transcranial focused ultrasound surgery in infants

2016· article· en· W2476772630 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMedical Physics · 2016
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsLakehead UniversityUniversity of TorontoCARE CanadaSickKids FoundationPhilips (Canada)Hospital for Sick Children
FundersFondation Brain CanadaFocused Ultrasound Foundation
KeywordsUltrasoundMedical physicsMagnetic resonance imagingMedicineRadiologyComputer scienceNuclear medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.720
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.250
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it