MétaCan
Menu
Back to cohort
Record W3026700267 · doi:10.1117/12.2560358

Full-size anthropomorphic phantom for 2D and 3D breast x-ray imaging

2020· article· en· W3026700267 on OpenAlexaff
James G. Mainprize, Gordon E. Mawdsley, Ann-Katherine Carton, Zhijin Li, R. Klausz, Serge Muller, Martin J. Yaffe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of TorontoSunnybrook Hospital
Fundersnot available
KeywordsImaging phantomMaterials scienceMammographyBiomedical engineeringBreast imagingDigital mammographyMedical imagingNuclear medicineComputer scienceBreast cancerMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Anthropomorphic breast phantoms are used to create images that mimic aspects of clinical breast images and are useful in optimization and characterization of breast imaging systems. Here, a full-sized compressed physical breast phantom is designed and manufactured with 100 m resolution, high reproducibility and x-ray properties similar to that of breast tissues. The phantom design is based on a digital model derived from the morphology and distribution of large, medium and small scale fibroglandular and inter-glandular adipose tissue observed in clinical breast computerized tomography (bCT) images. The physical phantom consists of four slabs of a polyamide-12 component that mimics adipose tissue fabricated using selective laser sintering (SLS). The fibroglandular component is a low viscosity resin doped with a small amount of zinc oxide nanoparticles (&lt;110 nm) to increase attenuation. The phantom was imaged on a Senographe Pristina and compared to image simulations of the virtual phantom. The power spectral parameter, &beta; was 3.8&plusmn;0.2 and 3.9&plusmn;0.5 for the physical and virtual phantoms in a digital mammogram. The corresponding Laplacian fractional entropy (LFE) averaged 0.22 and 0.14 across the range 0.125–1.29 mm<sup>-1</sup>. Very good texture cancellation was obtained in contrast-enhanced spectral mammography.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.641

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced X-ray and CT ImagingFrench-language works237,207