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Record W4377288532 · doi:10.1364/boe.491538

High-resolution multi-z confocal microscopy with a diffractive optical element

2023· article· en· W4377288532 on OpenAlexaff
Bingying Zhao, Minoru Koyama, Jérôme Mertz

Bibliographic record

VenueBiomedical Optics Express · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsUniversity of Toronto
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of HealthNational Science Foundation
KeywordsMicroscopeConfocalConfocal microscopyMicroscopyOpticsFluorescence microscopeMaterials scienceResolution (logic)Optical microscopeImage resolutionSuper-resolution microscopyOptical sectioningLight sheet fluorescence microscopyScanning confocal electron microscopyComputer sciencePhysicsFluorescenceArtificial intelligenceScanning electron microscope

Abstract

fetched live from OpenAlex

There has been recent interest in the development of fluorescence microscopes that provide high-speed volumetric imaging for life-science applications. For example, multi-z confocal microscopy enables simultaneous optically-sectioned imaging at multiple depths over relatively large fields of view. However, to date, multi-z microscopy has been hampered by limited spatial resolution owing to its initial design. Here we present a variant of multi-z microscopy that recovers the full spatial resolution of a conventional confocal microscope while retaining the simplicity and ease of use of our initial design. By introducing a diffractive optical element in the illumination path of our microscope, we engineer the excitation beam into multiple tightly focused spots that are conjugated to axially distributed confocal pinholes. We discuss the performance of this multi-z microscope in terms of resolution and detectability and demonstrate its versatility by performing in-vivo imaging of beating cardiomyocytes in engineered heart tissues and neuronal activity in c. elegans and zebrafish brains.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.163
Threshold uncertainty score0.905

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 studies0.0000.001
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 head0.297
Teacher spread0.286 · 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
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

Citations7
Published2023
Admission routes1
Has abstractyes

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