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Experimental Phase and Dielectric-Based Comparison of Propagation Speed in Microwave Breast Imaging

2025· article· W4417131696 on OpenAlexaff
Illia Prykhodko, Stephen Pistorius

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMicrowave imagingMicrowaveCorrelation coefficientPhase (matter)DielectricHomogeneousWave propagationIterative reconstruction

Abstract

fetched live from OpenAlex

Microwave Breast Imaging is a promising imaging modality that leverages differences in the dielectric properties between breast tissues to provide affordable and reliable breast cancer screening. Accurate image reconstruction requires the microwave propagation speed in the tissues. The propagation speed can be obtained by analyzing the phase of the wave transmitted through the media. An experimental bistatic geometry was used to collect <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S_{21}$</tex> data for various tissue-mimicking liquids. The propagation speeds were extracted using the phase-based method and were compared to those obtained from dielectric measurements of the materials. The propagation speed was underestimated across the liquids, and a linear frequency-dependent correction was applied above 4 GHz to minimize the effects of noise. The Concordance Correlation Coefficient (CCC) and Pearson Correlation Coefficient (PCC) analysis showed good agreement for both DGBE 95% and DGBE 70% with CCC over 0.920 and PCC over 0.982, followed by DGBE 90 %. Glycerin was associated with the lowest values of CCC and PCC, 0.849 and 0.906, respectively, but there was still reasonable agreement between the theory and the experimental values. This study demonstrates the potential of the phase-based method to enhance MBI image reconstruction, moving away from the homogeneous approach and towards more precise, data-driven techniques.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.285
Teacher spread0.276 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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