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A Quantitative Analysis of the Plastic Shell Effects in 3D- Printed Breast Phantoms for Microwave Imaging

2022· article· en· W4281570925 on OpenAlexaff
Tyson Reimer, Spencer Christie, Stephen Pistorius

Bibliographic record

Venue2022 16th European Conference on Antennas and Propagation (EuCAP) · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsImaging phantom3d printedMaterials scienceMicrowaveBiomedical engineeringMicrowave imagingShell (structure)3d printerPermittivityDielectricComposite materialComputer scienceNuclear medicineOptoelectronicsMedicine

Abstract

fetched live from OpenAlex

3D-printed breast phantoms have been used to evaluate methods in breast microwave sensing (BMS). These phantoms use 3D-printed shells from MRI images to hold liquids that mimic the dielectric properties of breast tissues. The MRI-derived shells allow the phantoms to mimic the morphology of in vivo tissues and the liquids mimic the microwave properties of the tissues. The use of low-permittivity 3D-printable plastics in the shells poses a potential challenge, as the plastics do not mimic the properties of breast tissues. This work quantitatively investigated the effects of eight plastic fibroglandular shells. The statistical analysis in this work indicated that three of the eight phantom shells did not produce significant reflections. These results indicate that, depending on the specific phantom shell design, the utility of 3D-printed phantoms may be limited by using low-permittivity plastics, and further examination of the role of plastics in 3D-printed phantoms should be performed.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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 designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

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