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Record W2907158509 · doi:10.1109/ultsym.2018.8580074

An Automated Breast Ultrasound Scanner with Integrated Shear Wave Elastography, Doppler Flow Imaging and Photoacoustic Tomography

2018· article· en· W2907158509 on OpenAlexaff
Corey J. Kelly, Julio Lobo, Mohammad Honarvar, Yanan Shao, Septimiu E. Salcudean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImaging phantomElastographyBiomedical engineeringScannerTomographyMaterials scienceUltrasoundDoppler effectPhotoacoustic Doppler effectElasticity (physics)Shear wavesRadiologyMedicineShear (geology)OpticsPhysics

Abstract

fetched live from OpenAlex

We have integrated shear wave elasticity, Doppler flow imaging, and photoacoustic tomography into the SonixEm-brace automated breast ultrasound scanner (ABUS). This system can acquire a multimodal volumetric scan of the entire breast in under ten minutes. We present here a series of phantom studies demonstrating the capabilities of this combined system for the first time. We imaged a commercially available elastography training phantom, measuring the stiffness of the background material to be 17.5 ± 0.1 kPa and the stiffness of the inclusions to be 32.3 ± 0.3 kPa, as compared to the manufacturer's provided values of 20 ± 5 kPa and ~40 kPa, respectively. We imaged a knotted tube phantom, demonstrating the computation and scan conversion of volumetric Doppler flow data. Finally, we developed and imaged a multimodal phantom incorporating inclusions which are uniquely visible in either elasticity or photoacoustic imaging.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.003
GPT teacher head0.196
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2018
Admission routes1
Has abstractyes

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