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Record W2034090063 · doi:10.1364/boe.6.001003

Photoacoustic radar phase-filtered spatial resolution and co-registered ultrasound image enhancement for tumor detection

2015· article· en· W2034090063 on OpenAlexafffund
Edem Dovlo, Bahman Lashkari, Andreas Mandelis, Wei Shi, Fei‐Fei Liu

Bibliographic record

VenueBiomedical Optics Express · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsPrincess Margaret Cancer CentreOntario Institute for Cancer ResearchUniversity of Toronto
FundersSamsungNatural Sciences and Engineering Research Council of CanadaSamsung Advanced Institute of Technology
KeywordsPhotoacoustic imaging in biomedicineImage resolutionUltrasoundMaterials scienceOpticsBiomedical engineeringPhase (matter)Photoacoustic spectroscopyRadarRadiologyMedical physicsMedicineComputer sciencePhysics

Abstract

fetched live from OpenAlex

Co-registered ultrasound (US) and frequency-domain photoacoustic radar (FD-PAR) imaging is reported for the first time in this paper. The merits of ultrasound and cross-correlation (radar) frequency-domain photoacoustic imaging are leveraged for accurate tumor detection. Commercial US imagers possess sophisticated, optimized software for rapid image acquisition that could dramatically speed-up PA imaging. The PAR image generated from the amplitude of the cross-correlation between detected and input signals was filtered by the standard deviation (SD) of the phase of the correlation signal, resulting in strong improvement of image spatial resolution, signal-to-noise ratio (SNR) and contrast. Application of phase-mediated image improvement is illustrated by imaging a cancer cell-injected mouse. A 14-15 dB SNR gain was recorded for the phase-filtered image compared to the amplitude and phase independently, while ~340 μm spatial resolution was seen for the phase PAR image compared to ~840 μm for the amplitude 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 categoriesnone
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 score0.854
Threshold uncertainty score0.975

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 head0.024
GPT teacher head0.276
Teacher spread0.252 · 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 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

Citations13
Published2015
Admission routes2
Has abstractyes

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