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A tissue stabilization device for MRI-guided breast biopsy

2014· article· en· W2055227274 on OpenAlexaff
Alexandru Patriciu, Maggie Chen, Behzad Iranpanah, Shahin Sirouspour

Bibliographic record

VenueMedical Engineering & Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBiopsyBreast biopsyFlexibility (engineering)Magnetic resonance imagingComputer scienceBiomedical engineeringCompression (physics)Displacement (psychology)Materials scienceMedicineRadiologyMammographyMathematicsBreast cancer

Abstract

fetched live from OpenAlex

We present a breast tissue stabilization device that can be used in magnetic resonance imaging-guided biopsy. The device employs adjustable support plates with an optimized geometry to minimize the biopsy target displacement using smaller compression than the conventional parallel plates approach. It is expected that the reduced compression will cause less patient discomfort and improve image quality by enhancing the contrast intake. Precomputed optimal positions of the stabilization plates for a given biopsy target location are provided in a look-up table. The results of several experiments with a prototype of the device carried out on chicken breast tissue demonstrate the effectiveness of the new design when compared with conventional stabilization methods. The proposed stabilization mechanism provides excellent flexibility in selecting the needle insertion point and can be used in manual as well as robot-assisted biopsy 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 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)0.000
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
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.009
GPT teacher head0.240
Teacher spread0.230 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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