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Record W2479903757 · doi:10.1118/1.4959539

Technical Note: Robust measurement of the slice-sensitivity profile in breast tomosynthesis

2016· article· en· W2479903757 on OpenAlexafffund
Aili K. Maki, James G. Mainprize, Martin J. Yaffe

Bibliographic record

VenueMedical Physics · 2016
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Cancer Society
KeywordsTomosynthesisFull width at half maximumRepeatabilityImaging phantomCartesian coordinate systemMammographyNuclear medicineRobustness (evolution)OpticsMaterials scienceMathematicsPhysicsBreast cancerMedicineStatisticsGeometry

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this work is to improve the repeatability of the measurement of the slice-sensitivity profile (SSP) in reconstructed breast tomosynthesis volumes. METHODS: A grid of aluminum ball-bearings (BBs) within a PMMA phantom was imaged on breast tomosynthesis systems from three different manufacturers. The full-width half-maximum (FWHM) values were measured for the SSPs of the BBs in the reconstructed volumes. The effect of transforming the volumes from a Cartesian coordinate system (CCS) to a cone-beam coordinate system (CBCS) on the variability in the FWHM values was assessed. RESULTS: Transforming the volumes from a CCS to a CBCS before measuring the SSPs reduced the coefficient of variation (COV) in the measurements of FWHM in repeated measurements by 56% and reduced the dependence of the FWHM values on the location of the BBs within the reconstructed volume by 76%. CONCLUSIONS: Measuring the SSP in the volumes in a CBCS improves the robustness of the measurement.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.018
GPT teacher head0.239
Teacher spread0.221 · 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 designOther design
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

Citations8
Published2016
Admission routes2
Has abstractyes

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