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Record W4297014982 · doi:10.1364/boe.471770

Deformable mirror-based photoacoustic remote sensing (PARS) microscopy for depth scanning

2022· article· en· W4297014982 on OpenAlexafffund
Lyazzat Mukhangaliyeva, Samed Kocer, Alkris Warren, Kevan Bell, Marian Boktor, Mustafa Yavuz, Eihab Abdel‐Rahman, Parsin Haji Reza

Bibliographic record

VenueBiomedical Optics Express · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Waterloo
FundersCentre for Bioengineering and Biotechnology, University of WaterlooMitacsUniversity of WaterlooillumiSonicsCanada Foundation for InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsMaterials scienceCardinal pointMicroscopyFocus (optics)Photoacoustic imaging in biomedicineBiomedical engineeringPhysicsMedicine

Abstract

fetched live from OpenAlex

Optically shifting the focal plane to allow depth scanning of delicate biological structures and processes in their natural environment offers an appealing alternative to conventional mechanical scanning. Our technique uses a deformable mirror-based photoacoustic remote sensing microscopy (PARS) with a focus shifting of Δz ∼ 240 µm. We achieve this by integrating a deformable mirror that functions as a varifocal mirror for axial scanning. First, the system's focal shift capability was demonstrated with USAF resolution targets and carbon fiber phantoms, followed by in-vivo visualizations of blood vessels in chicken embryo chorioallantoic membrane (CAM). This work represents an initial step toward developing a non-contact, label-free, and aberration-free PARS imaging system with axial scanning capability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.250
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations11
Published2022
Admission routes2
Has abstractyes

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