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Record W2161368485 · doi:10.1109/tbme.2011.2134096

An MRI-Compatible Robotic System With Hybrid Tracking for MRI-Guided Prostate Intervention

2011· article· en· W2161368485 on OpenAlexaff
Axel Krieger, Iulian Iordachita, Peter Guion, Anurag K. Singh, A. Kaushal, Cynthia Ménard, Peter A. Pinto, Kevin Camphausen, Gábor Fichtinger, Louis L. Whitcomb

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsQueen's UniversityPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Institute of Biomedical Imaging and Bioengineering
KeywordsFiducial markerImaging phantomScannerInterfacingMagnetic resonance imagingComputer scienceTracking (education)Computer visionTracking systemArtificial intelligenceProstateInterventional magnetic resonance imagingBiomedical engineeringMedicineComputer hardwareKalman filterNuclear medicineRadiology

Abstract

fetched live from OpenAlex

This paper reports the development, evaluation, and first clinical trials of the access to the prostate tissue (APT) II system-a scanner independent system for magnetic resonance imaging (MRI)-guided transrectal prostate interventions. The system utilizes novel manipulator mechanics employing a steerable needle channel and a novel six degree-of-freedom hybrid tracking method, comprising passive fiducial tracking for initial registration and subsequent incremental motion measurements. Targeting accuracy of the system in prostate phantom experiments and two clinical human-subject procedures is shown to compare favorably with existing systems using passive and active tracking methods. The portable design of the APT II system, using only standard MRI image sequences and minimal custom scanner interfacing, allows the system to be easily used on different MRI scanners.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.020
GPT teacher head0.230
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations99
Published2011
Admission routes1
Has abstractyes

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