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Record W2581160999 · doi:10.1109/tuffc.2017.2661238

Photoacoustic–Ultrasound Tomography With S-Sequence Aperture Encoding

2017· article· en· W2581160999 on OpenAlexafffund
Quinn Barber, Roger J. Zemp

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2017
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Cancer Society Research InstituteCanadian Institutes of Health Research
KeywordsImaging phantomCoded apertureTomographyOpticsAperture (computer memory)Encoding (memory)UltrasoundPhotoacoustic imaging in biomedicineImage resolutionDiffractionPhysicsMaterials scienceAcousticsComputer scienceArtificial intelligenceDetector

Abstract

fetched live from OpenAlex

A combined photoacoustic-ultrasound (PAUS) tomography system is introduced using ring-array and novel aperture encoding schemes. The ultrasound subsystem is able to achieve diffraction limited half-wavelength isotropic in-plane spatial resolution unlike previous systems. S-sequence aperture encoding improves signal-to-noise ratio (SNR) for the ultrasound tomography (UST) subsystem. We measured an average resolution of [Formula: see text] for S-sequence UST and a resolution of [Formula: see text] for photoacoustic tomography. We were able to measure SNR improvement using S-sequence spatial encoding using tissue-mimicking phantoms, and we displayed a composite PAUS phantom image.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.211
Teacher spread0.201 · 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 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

Citations7
Published2017
Admission routes2
Has abstractyes

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