The Airyscan detector from ZEISS: confocal imaging with improved signal-to-noise ratio and super-resolution View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-12

AUTHORS

Joseph Huff

ABSTRACT

The novel detector design of Airyscan overcomes limitations of the classical assembly consisting of a physical pinhole and a unitary detector and uses a new pinhole-plane image-detection approach based on a 32-channel GaAsP-PMT area detector. In Airyscan, each of the 32 detector elements acts as its own small pinhole with positional information. The new positional information allows for increased contrast of high-spatial-frequency information previously not available in traditional confocal systems. The increase in spatial-frequency contrast enables Airyscan to produce images with substantially increased SNR and resolution compared to LSM images acquired with a 1-AU pinhole. Ultimately, Airyscan delivers 1.7× higher resolution in all three spatial dimensions and increases the SNR by 4–8× compared with traditional LSM systems with a 1-AU pinhole. More... »

PAGES

1205

References to SciGraph publications

  • 2005-12. Optical sectioning microscopy in NATURE METHODS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nmeth.f.388

    DOI

    http://dx.doi.org/10.1038/nmeth.f.388

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1018861370


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Carl Zeiss (United States)", 
              "id": "https://www.grid.ac/institutes/grid.422866.c", 
              "name": [
                "Carl Zeiss Microscopy, LLC, Thornwood, New York, USA."
              ], 
              "type": "Organization"
            }, 
            "familyName": "Huff", 
            "givenName": "Joseph", 
            "id": "sg:person.010462774571.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010462774571.49"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.tim.2014.12.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018787041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth815", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020736869", 
              "https://doi.org/10.1038/nmeth815"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth815", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020736869", 
              "https://doi.org/10.1038/nmeth815"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1364/ol.38.002889", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1065234194"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2015-12", 
        "datePublishedReg": "2015-12-01", 
        "description": "The novel detector design of Airyscan overcomes limitations of the classical assembly consisting of a physical pinhole and a unitary detector and uses a new pinhole-plane image-detection approach based on a 32-channel GaAsP-PMT area detector. In Airyscan, each of the 32 detector elements acts as its own small pinhole with positional information. The new positional information allows for increased contrast of high-spatial-frequency information previously not available in traditional confocal systems. The increase in spatial-frequency contrast enables Airyscan to produce images with substantially increased SNR and resolution compared to LSM images acquired with a 1-AU pinhole. Ultimately, Airyscan delivers 1.7\u00d7 higher resolution in all three spatial dimensions and increases the SNR by 4\u20138\u00d7 compared with traditional LSM systems with a 1-AU pinhole.", 
        "genre": "non_research_article", 
        "id": "sg:pub.10.1038/nmeth.f.388", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1033763", 
            "issn": [
              "1548-7091", 
              "1548-7105"
            ], 
            "name": "Nature Methods", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "12", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "12"
          }
        ], 
        "name": "The Airyscan detector from ZEISS: confocal imaging with improved signal-to-noise ratio and super-resolution", 
        "pagination": "1205", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "59de45efa93035b5c532c10c2a1036e6a85fa31ec9cc1bc9f2b2d7ff4f66deaf"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/nmeth.f.388"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1018861370"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/nmeth.f.388", 
          "https://app.dimensions.ai/details/publication/pub.1018861370"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T18:09", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8675_00000435.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://www.nature.com/articles/nmeth.f.388"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/nmeth.f.388'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/nmeth.f.388'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/nmeth.f.388'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/nmeth.f.388'


     

    This table displays all metadata directly associated to this object as RDF triples.

    71 TRIPLES      21 PREDICATES      30 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/nmeth.f.388 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Nd1ceaa60d77d4b46bfee61c06bc40f52
    4 schema:citation sg:pub.10.1038/nmeth815
    5 https://doi.org/10.1016/j.tim.2014.12.010
    6 https://doi.org/10.1364/ol.38.002889
    7 schema:datePublished 2015-12
    8 schema:datePublishedReg 2015-12-01
    9 schema:description The novel detector design of Airyscan overcomes limitations of the classical assembly consisting of a physical pinhole and a unitary detector and uses a new pinhole-plane image-detection approach based on a 32-channel GaAsP-PMT area detector. In Airyscan, each of the 32 detector elements acts as its own small pinhole with positional information. The new positional information allows for increased contrast of high-spatial-frequency information previously not available in traditional confocal systems. The increase in spatial-frequency contrast enables Airyscan to produce images with substantially increased SNR and resolution compared to LSM images acquired with a 1-AU pinhole. Ultimately, Airyscan delivers 1.7× higher resolution in all three spatial dimensions and increases the SNR by 4–8× compared with traditional LSM systems with a 1-AU pinhole.
    10 schema:genre non_research_article
    11 schema:inLanguage en
    12 schema:isAccessibleForFree false
    13 schema:isPartOf N3b0950873d124c7db71cadfd51b03c57
    14 N82d1228651a248d5be0e05f19c69ea03
    15 sg:journal.1033763
    16 schema:name The Airyscan detector from ZEISS: confocal imaging with improved signal-to-noise ratio and super-resolution
    17 schema:pagination 1205
    18 schema:productId N7ea54694712e49aeba4a54741ef5e8a5
    19 N9e083dd4a99c48599c0f771b38b1399b
    20 Nc252033c413841e5a2c6b35f051e3b9c
    21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018861370
    22 https://doi.org/10.1038/nmeth.f.388
    23 schema:sdDatePublished 2019-04-10T18:09
    24 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    25 schema:sdPublisher Ne3d6150f415544f69022274256d77775
    26 schema:url https://www.nature.com/articles/nmeth.f.388
    27 sgo:license sg:explorer/license/
    28 sgo:sdDataset articles
    29 rdf:type schema:ScholarlyArticle
    30 N3b0950873d124c7db71cadfd51b03c57 schema:volumeNumber 12
    31 rdf:type schema:PublicationVolume
    32 N7ea54694712e49aeba4a54741ef5e8a5 schema:name readcube_id
    33 schema:value 59de45efa93035b5c532c10c2a1036e6a85fa31ec9cc1bc9f2b2d7ff4f66deaf
    34 rdf:type schema:PropertyValue
    35 N82d1228651a248d5be0e05f19c69ea03 schema:issueNumber 12
    36 rdf:type schema:PublicationIssue
    37 N9e083dd4a99c48599c0f771b38b1399b schema:name dimensions_id
    38 schema:value pub.1018861370
    39 rdf:type schema:PropertyValue
    40 Nc252033c413841e5a2c6b35f051e3b9c schema:name doi
    41 schema:value 10.1038/nmeth.f.388
    42 rdf:type schema:PropertyValue
    43 Nd1ceaa60d77d4b46bfee61c06bc40f52 rdf:first sg:person.010462774571.49
    44 rdf:rest rdf:nil
    45 Ne3d6150f415544f69022274256d77775 schema:name Springer Nature - SN SciGraph project
    46 rdf:type schema:Organization
    47 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    48 schema:name Information and Computing Sciences
    49 rdf:type schema:DefinedTerm
    50 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    51 schema:name Artificial Intelligence and Image Processing
    52 rdf:type schema:DefinedTerm
    53 sg:journal.1033763 schema:issn 1548-7091
    54 1548-7105
    55 schema:name Nature Methods
    56 rdf:type schema:Periodical
    57 sg:person.010462774571.49 schema:affiliation https://www.grid.ac/institutes/grid.422866.c
    58 schema:familyName Huff
    59 schema:givenName Joseph
    60 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010462774571.49
    61 rdf:type schema:Person
    62 sg:pub.10.1038/nmeth815 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020736869
    63 https://doi.org/10.1038/nmeth815
    64 rdf:type schema:CreativeWork
    65 https://doi.org/10.1016/j.tim.2014.12.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018787041
    66 rdf:type schema:CreativeWork
    67 https://doi.org/10.1364/ol.38.002889 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065234194
    68 rdf:type schema:CreativeWork
    69 https://www.grid.ac/institutes/grid.422866.c schema:alternateName Carl Zeiss (United States)
    70 schema:name Carl Zeiss Microscopy, LLC, Thornwood, New York, USA.
    71 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...