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Record W2126862907 · doi:10.1109/iembs.2009.5334452

High frequency optoacoustic microscopy

2009· article· en· W2126862907 on OpenAlexafffund
Wolfgang Bost, Frank Stracke, Eike C. Weiss, Shankar Narasimhan, Michael C. Kolios, Robert Lemor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsToronto Metropolitan University
FundersCanadian Institutes of Health Research
KeywordsMaterials scienceLaserMicroscopyOpticsPhotoacoustic effectResolution (logic)SIGNAL (programming language)Image resolutionTransducerOptoelectronicsComputer sciencePhysicsAcousticsArtificial intelligence

Abstract

fetched live from OpenAlex

Photoacoustic imaging--also called optoacoustic imaging--is a new hybrid modality of high tissue contrast which is based on the varying optical properties of tissue. The acoustic signal generated by pulsed laser absorption reports tissue-specific information with high spatial resolution. To increase the intrinsic contrast in tissue, absorbing particles are of great interest for optical imaging because of their considerable capacity to absorb and scatter light at visible and near-infrared wavelengths. The aim of the work presented here is to establish a scalable photoacoustic technology for volume imaging of biological samples down to diffraction limited microscopy. For this purpose a versatile photoacoustic microscopy platform has been developed with unmatched spatial resolution consisting of a microchip laser and a measurement cell with different transducers attached allowing generation and detection of laser-induced ultrasound signals in a frequency range up to 400 MHz. The performance of a versatile photoacoustic microscopy platform was evaluated via 2D optoacoustic images of light absorbing microparticles (5 microm Fe(3)0(4) and 1 micromblack toner particles) embedded in a polystyrene matrix. High frequency signals in the frequency range of 400 MHz generated by a single 1 microm particle could be recorded with a high signal to noise ratio (SNR) of 34 dB.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.607
Threshold uncertainty score0.456

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 head0.004
GPT teacher head0.204
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.

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

Citations19
Published2009
Admission routes2
Has abstractyes

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