Motion Estimation and Compensation Strategies in Dynamic Computerized Tomography View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-02-04

AUTHORS

Bernadette N. Hahn

ABSTRACT

A main challenge in computerized tomography consists in imaging moving objects. Temporal changes during the measuring process lead to inconsistent data sets, and applying standard reconstruction techniques causes motion artefacts which can severely impose a reliable diagnostics. Therefore, novel reconstruction techniques are required which compensate for the dynamic behavior. This article builds on recent results from a microlocal analysis of the dynamic setting, which enable us to formulate efficient analytic motion compensation algorithms for contour extraction. Since these methods require information about the dynamic behavior, we further introduce a motion estimation approach which determines parameters of affine and certain non-affine deformations directly from measured motion-corrupted Radon-data. Our methods are illustrated with numerical examples for both types of motion. More... »

PAGES

10

References to SciGraph publications

  • 1980. Introduction to Pseudodifferential and Fourier Integral Operators, Pseudodifferential Operators in NONE
  • 2012. Combined Motion Estimation and Reconstruction in Tomography in COMPUTER VISION – ECCV 2012. WORKSHOPS AND DEMONSTRATIONS
  • 2015. Microlocal Analysis in Tomography in HANDBOOK OF MATHEMATICAL METHODS IN IMAGING
  • 1986. The Mathematics of Computerized Tomography in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11220-017-0159-6

    DOI

    http://dx.doi.org/10.1007/s11220-017-0159-6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1083540028


    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/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "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"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Institute of Mathematics, University of W\u00fcrzburg, 97074, W\u00fcrzburg, Germany", 
              "id": "http://www.grid.ac/institutes/grid.8379.5", 
              "name": [
                "Institute of Mathematics, University of W\u00fcrzburg, 97074, W\u00fcrzburg, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hahn", 
            "givenName": "Bernadette N.", 
            "id": "sg:person.012404547650.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012404547650.51"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-663-01409-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1111507219", 
              "https://doi.org/10.1007/978-3-663-01409-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-33863-2_2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053597014", 
              "https://doi.org/10.1007/978-3-642-33863-2_2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4939-0790-8_36", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020216009", 
              "https://doi.org/10.1007/978-1-4939-0790-8_36"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4684-8780-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002495009", 
              "https://doi.org/10.1007/978-1-4684-8780-0"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-02-04", 
        "datePublishedReg": "2017-02-04", 
        "description": "A main challenge in computerized tomography consists in imaging moving objects. Temporal changes during the measuring process lead to inconsistent data sets, and applying standard reconstruction techniques causes motion artefacts which can severely impose a reliable diagnostics. Therefore, novel reconstruction techniques are required which compensate for the dynamic behavior. This article builds on recent results from a microlocal analysis of the dynamic setting, which enable us to formulate efficient analytic motion compensation algorithms for contour extraction. Since these methods require information about the dynamic behavior, we further introduce a motion estimation approach which determines parameters of affine and certain non-affine deformations directly from measured motion-corrupted Radon-data. Our methods are illustrated with numerical examples for both types of motion.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s11220-017-0159-6", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1031930", 
            "issn": [
              "1566-0184", 
              "1573-9317"
            ], 
            "name": "Sensing and Imaging", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "18"
          }
        ], 
        "keywords": [
          "dynamic behavior", 
          "motion estimation approach", 
          "non-affine deformation", 
          "motion compensation algorithm", 
          "inconsistent data sets", 
          "motion estimation", 
          "reconstruction technique", 
          "compensation algorithm", 
          "contour extraction", 
          "novel reconstruction technique", 
          "compensation strategy", 
          "data sets", 
          "main challenges", 
          "process lead", 
          "types of motion", 
          "standard reconstruction technique", 
          "dynamic setting", 
          "estimation approach", 
          "numerical examples", 
          "motion artifacts", 
          "deformation", 
          "reliable diagnostics", 
          "algorithm", 
          "behavior", 
          "objects", 
          "technique", 
          "motion", 
          "method", 
          "information", 
          "set", 
          "parameters", 
          "artifacts", 
          "estimation", 
          "extraction", 
          "challenges", 
          "compensates", 
          "radon data", 
          "dynamic computerized tomography", 
          "recent results", 
          "example", 
          "diagnostics", 
          "affine", 
          "results", 
          "lead", 
          "temporal changes", 
          "approach", 
          "microlocal analysis", 
          "strategies", 
          "analysis", 
          "types", 
          "tomography", 
          "setting", 
          "article", 
          "changes", 
          "computerized tomography"
        ], 
        "name": "Motion Estimation and Compensation Strategies in Dynamic Computerized Tomography", 
        "pagination": "10", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1083540028"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11220-017-0159-6"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11220-017-0159-6", 
          "https://app.dimensions.ai/details/publication/pub.1083540028"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:36", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_744.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s11220-017-0159-6"
      }
    ]
     

    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.10.1007/s11220-017-0159-6'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11220-017-0159-6'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11220-017-0159-6'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11220-017-0159-6'


     

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

    128 TRIPLES      21 PREDICATES      82 URIs      70 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11220-017-0159-6 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N073cb086b9324125b0256eac8277f9f4
    4 schema:citation sg:pub.10.1007/978-1-4684-8780-0
    5 sg:pub.10.1007/978-1-4939-0790-8_36
    6 sg:pub.10.1007/978-3-642-33863-2_2
    7 sg:pub.10.1007/978-3-663-01409-6
    8 schema:datePublished 2017-02-04
    9 schema:datePublishedReg 2017-02-04
    10 schema:description A main challenge in computerized tomography consists in imaging moving objects. Temporal changes during the measuring process lead to inconsistent data sets, and applying standard reconstruction techniques causes motion artefacts which can severely impose a reliable diagnostics. Therefore, novel reconstruction techniques are required which compensate for the dynamic behavior. This article builds on recent results from a microlocal analysis of the dynamic setting, which enable us to formulate efficient analytic motion compensation algorithms for contour extraction. Since these methods require information about the dynamic behavior, we further introduce a motion estimation approach which determines parameters of affine and certain non-affine deformations directly from measured motion-corrupted Radon-data. Our methods are illustrated with numerical examples for both types of motion.
    11 schema:genre article
    12 schema:isAccessibleForFree false
    13 schema:isPartOf N2d8339bb95ae49f0902748e25dcd0d3a
    14 N962d4e69a9704b22a34bf9d1aff5b593
    15 sg:journal.1031930
    16 schema:keywords affine
    17 algorithm
    18 analysis
    19 approach
    20 article
    21 artifacts
    22 behavior
    23 challenges
    24 changes
    25 compensates
    26 compensation algorithm
    27 compensation strategy
    28 computerized tomography
    29 contour extraction
    30 data sets
    31 deformation
    32 diagnostics
    33 dynamic behavior
    34 dynamic computerized tomography
    35 dynamic setting
    36 estimation
    37 estimation approach
    38 example
    39 extraction
    40 inconsistent data sets
    41 information
    42 lead
    43 main challenges
    44 method
    45 microlocal analysis
    46 motion
    47 motion artifacts
    48 motion compensation algorithm
    49 motion estimation
    50 motion estimation approach
    51 non-affine deformation
    52 novel reconstruction technique
    53 numerical examples
    54 objects
    55 parameters
    56 process lead
    57 radon data
    58 recent results
    59 reconstruction technique
    60 reliable diagnostics
    61 results
    62 set
    63 setting
    64 standard reconstruction technique
    65 strategies
    66 technique
    67 temporal changes
    68 tomography
    69 types
    70 types of motion
    71 schema:name Motion Estimation and Compensation Strategies in Dynamic Computerized Tomography
    72 schema:pagination 10
    73 schema:productId N726c059a85834f419bc8470684109fa1
    74 Nde4d7f3eb73b4927aa5d7913d75229d4
    75 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083540028
    76 https://doi.org/10.1007/s11220-017-0159-6
    77 schema:sdDatePublished 2022-12-01T06:36
    78 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    79 schema:sdPublisher Nb99c1bb6fdff4d249b3a7d47a71fcfff
    80 schema:url https://doi.org/10.1007/s11220-017-0159-6
    81 sgo:license sg:explorer/license/
    82 sgo:sdDataset articles
    83 rdf:type schema:ScholarlyArticle
    84 N073cb086b9324125b0256eac8277f9f4 rdf:first sg:person.012404547650.51
    85 rdf:rest rdf:nil
    86 N2d8339bb95ae49f0902748e25dcd0d3a schema:issueNumber 1
    87 rdf:type schema:PublicationIssue
    88 N726c059a85834f419bc8470684109fa1 schema:name doi
    89 schema:value 10.1007/s11220-017-0159-6
    90 rdf:type schema:PropertyValue
    91 N962d4e69a9704b22a34bf9d1aff5b593 schema:volumeNumber 18
    92 rdf:type schema:PublicationVolume
    93 Nb99c1bb6fdff4d249b3a7d47a71fcfff schema:name Springer Nature - SN SciGraph project
    94 rdf:type schema:Organization
    95 Nde4d7f3eb73b4927aa5d7913d75229d4 schema:name dimensions_id
    96 schema:value pub.1083540028
    97 rdf:type schema:PropertyValue
    98 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    99 schema:name Information and Computing Sciences
    100 rdf:type schema:DefinedTerm
    101 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    102 schema:name Artificial Intelligence and Image Processing
    103 rdf:type schema:DefinedTerm
    104 sg:journal.1031930 schema:issn 1566-0184
    105 1573-9317
    106 schema:name Sensing and Imaging
    107 schema:publisher Springer Nature
    108 rdf:type schema:Periodical
    109 sg:person.012404547650.51 schema:affiliation grid-institutes:grid.8379.5
    110 schema:familyName Hahn
    111 schema:givenName Bernadette N.
    112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012404547650.51
    113 rdf:type schema:Person
    114 sg:pub.10.1007/978-1-4684-8780-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002495009
    115 https://doi.org/10.1007/978-1-4684-8780-0
    116 rdf:type schema:CreativeWork
    117 sg:pub.10.1007/978-1-4939-0790-8_36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020216009
    118 https://doi.org/10.1007/978-1-4939-0790-8_36
    119 rdf:type schema:CreativeWork
    120 sg:pub.10.1007/978-3-642-33863-2_2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053597014
    121 https://doi.org/10.1007/978-3-642-33863-2_2
    122 rdf:type schema:CreativeWork
    123 sg:pub.10.1007/978-3-663-01409-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111507219
    124 https://doi.org/10.1007/978-3-663-01409-6
    125 rdf:type schema:CreativeWork
    126 grid-institutes:grid.8379.5 schema:alternateName Institute of Mathematics, University of Würzburg, 97074, Würzburg, Germany
    127 schema:name Institute of Mathematics, University of Würzburg, 97074, Würzburg, Germany
    128 rdf:type schema:Organization
     




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


    ...