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Record W2790167383 · doi:10.1109/tuffc.2018.2807699

Compressibility and Density Weighting for Ultrasound Scattering Tomography

2018· article· en· W2790167383 on OpenAlexafffund
Quinn Barber, Roger J. Zemp

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsCompressibilityWeightingTomographyScatteringAcousticsUltrasoundPhysicsMaterials scienceOpticsMechanics

Abstract

fetched live from OpenAlex

A novel reconstruction technique based on delay-and-sum ultrasound tomography algorithms is introduced. This reconstruction technique enables ultrasound tomographic systems to produce compressibility- and density-weighted images (D-WI) for spherically symmetric ultrasound scatterers. The efficacy of this reconstruction technique was demonstrated with simulation and phantom experiments. Separation between a dense wire target and a compressible thread target within a phantom was quantified, showing an increased signal of the wire target for D-WIs. The suppression of background scatter in D-WIs was also quantified. Resolution was calculated for these reconstruction techniques, exemplifying the half-wavelength diffraction-limited resolution capabilities of this ultrasound scattering tomography system. The proposed technique offers a enhanced minimum-detectable density-contrast sensitivity compared to traditional B-scan imaging for a 100- target. This enhanced detectability is expected to prove advantageous for microcalcification 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 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: none
Teacher disagreement score0.843
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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