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Record W1982333157 · doi:10.1109/tbme.2012.2192118

Biopsy Needle Artifact Localization in MRI-Guided Robotic Transrectal Prostate Intervention

2012· article· en· W1982333157 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.

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsQueen's University
FundersNational Institute of Biomedical Imaging and Bioengineering
KeywordsArtifact (error)ScannerMagnetic resonance imagingProstateMedicineRadiologyInterventional magnetic resonance imagingBiomedical engineeringNuclear medicineComputer scienceComputer visionArtificial intelligenceCancer

Abstract

fetched live from OpenAlex

Recently a number of robotic intervention systems for magnetic resonance image (MRI)-guided needle placement in the prostate have been reported. In MRI-guided needle interventions, after a needle is inserted, the needle position is often confirmed with a volumetric MRI scan. Commonly used titanium needles are not directly visible in an MRI, but they generate a susceptibility artifact in the immediate neighborhood of the needle. This paper reports the results of a quantitative study of the relationship between the true position of titanium biopsy needle and the corresponding needle artifact position in MRI, thereby providing a better understanding of the influence of needle artifact on targeting errors. The titanium needle tip artifact extended 9 mm beyond the actual needle tip location with tendency to bend toward the scanner's B (0) magnetic field direction, and axially displaced 0.38 and 0.32 mm (mean) in scanner's frequency and phase encoding direction, respectively.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.901

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 head0.011
GPT teacher head0.231
Teacher spread0.219 · 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