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Record W2800371741 · doi:10.1364/ol.43.002221

Sparsity-based reconstruction for super-resolved limited-view photoacoustic computed tomography deep in a scattering medium

2018· article· en· W2800371741 on OpenAlexafffund
David Egolf, Ryan K. W. Chee, Roger J. Zemp

Bibliographic record

VenueOptics Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlberta Innovates - Technology Futures
KeywordsOpticsPhotoacoustic imaging in biomedicineScatteringBeamformingSIGNAL (programming language)Iterative reconstructionTomographyImage resolutionResolution (logic)Materials scienceTransducerPhotoacoustic effectPhysicsAcousticsComputer scienceArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

Delay-and-sum beamforming (DSB) of photoacoustic data does not incorporate a priori spatial sparsity of the imaging target. By incorporating this information into beamforming for limited-view photoacoustic computed tomography, we experimentally obtained enhanced resolution images of wires at a depth of 8.5 mm in a tissue mimicking scattering medium. Using a 21 MHz transducer, we improved the resolution from the 200 to 250 μm achieved by DSB to 75 μm. The sparsity-based technique also generated a cleaner image with a background signal level of roughly -50 dB, much lower than the roughly -18 dB background signal level of DSB.

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 score0.816
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 studies0.0000.000
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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations26
Published2018
Admission routes2
Has abstractyes

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