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Record W2060273996 · doi:10.1109/isbi.2012.6235490

Microwave radar imaging of inhomogeneous breast phantoms using circular holography

2012· article· en· W2060273996 on OpenAlexaff
Daniel Flores‐Tapia, Oleksandr Maizlish, Clive Alabaster, Stephen Pistorius

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsHolographyMicrowave imagingIterative reconstructionRadarImaging phantomMicrowaveMagnetic resonance imagingNoise (video)Radar imagingComputer scienceBiomedical engineeringOpticsPhysicsComputer visionImage (mathematics)MedicineRadiologyTelecommunications

Abstract

fetched live from OpenAlex

Circular holography is a novel reconstruction technique for Breast Microwave Radar (BMR) imaging. Compared to current state of the art BMR image formation methods, this reconstruction approach yields spatially accurate images with higher signal to noise ratios and no artifacts. Nevertheless, a preclinical study is required to assess the feasibility of this technique in realistic breast imaging scenarios. In this paper, a series of preliminary results showing the performance of circular holography on preclinical datasets are presented. These datasets were recorded from inhomogeneous breast phantoms that mimic the dielectric properties and the anatomy of the different breast tissues. These phantoms were fabricated using Magnetic Resonance (MR) images a base model to emulate the shape and volumes of dense tissue regions. The reconstructed BMR images show that tumor and fibroglandular tissue responses can be effectively distinguished, suggesting that circular holography can be used as BMR reconstruction approach in clinical scenarios.

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 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: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.209
Teacher spread0.201 · 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 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

Citations5
Published2012
Admission routes1
Has abstractyes

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