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Record W2036424832 · doi:10.1109/tmech.2014.2341657

A Pneumatically Actuated Target Stabilization Device for MRI-Guided Breast Biopsy

2014· article· en· W2036424832 on OpenAlexafffund
Behzad Iranpanah, Maggie Chen, Alexandru Patriciu, Shahin Sirouspour

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2014
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnetic resonance imagingBiomedical engineeringActuatorComputer scienceMaterials scienceBiopsyPneumatic actuatorChicken breastMagnetBreast biopsyRadiologyMedicineArtificial intelligenceMammographyBreast cancerMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

This paper introduces a novel tissue stabilization device for magnetic resonance imaging (MRI)-guided breast biopsy. The device is comprised of two pneumatically actuated support plates that would stabilize the biopsy target movements during needle insertion. An optimized geometry for the support plates allows for a good degree of tissue stabilization without relying on large compression forces. The plate configuration can also be adjusted inside the magnet bore using pneumatic actuators driven by pressure-controlled valves that are placed in the MRI control room. This capability allows for the compensation of the target displacement based on MRI image feedback. When combined with a separate needle drive mechanism, this stabilization device would enable in-bore MRI-guided breast biopsy. Experiments performed on chicken breast tissue with a prototype of the device demonstrate the effectiveness of this mechanism in increasing needle targeting accuracy using two simple error correction strategies. Furthermore, MRI compatibility tests are carried out to assess the performance of the device inside MRI.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations13
Published2014
Admission routes2
Has abstractyes

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