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Record W2598791167 · doi:10.1115/1.4036338

Assessment of the Accuracy of Optical Shape Sensing for Needle Tracking Interventions

2017· article· en· W2598791167 on OpenAlexafffund
Koushik Kanti Mandal, François Parent, Raman Kashyap, Sylvain Martel, Samuel Kadoury

Bibliographic record

VenueJournal of Medical Devices · 2017
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsPolytechnique Montréal
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsImaging phantomTracking (education)Magnetic resonance imagingTracking systemComputer visionAblationFiber Bragg gratingPercutaneousBiomedical engineeringComputer scienceArtificial intelligenceOptical fiberRadiologyMedicineOpticsPhysics

Abstract

fetched live from OpenAlex

Accurate needle guidance is essential for a number of magnetic resonance imaging (MRI)-guided percutaneous procedures, such as radiofrequency ablation (RFA) of metastatic liver tumors. A promising technology to obtain real-time tracking of the shape and tip of a needle is by using high-frequency (up to 20 kHz) fiber Bragg grating (FBG) sensors embedded in optical fibers, which are insensitive to external magnetic fields. We fabricated an MRI-compatible needle designed for percutaneous procedures with a series of FBG sensors which would be tracked in an image-guidance system, allowing to display the needle shape within a navigation image. A series of phantom experiments demonstrated needle tip tracking errors of 1.05 ± 0.08 mm for a needle deflection up to 16.82 mm on a ground-truth model and showed nearly similar accuracy to electromagnetic (EM) tracking (i.e., 0.89 ± 0.09 mm). We demonstrated feasibility of the FBG-based tracking system for MRI-guided interventions with differences under 1 mm between tracking systems. This study establishes the needle tracking accuracy of FBG needle tracking for image-guided procedures.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.060
GPT teacher head0.390
Teacher spread0.330 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations4
Published2017
Admission routes2
Has abstractyes

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