Neointimal formation after carotid artery stenting: phantom and clinical evaluation of model-based iterative reconstruction (MBIR) View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-01

AUTHORS

Kazushi Yokomachi, Fuminari Tatsugami, Toru Higaki, Shinji Kume, Shigeyuki Sakamoto, Takahito Okazaki, Kaoru Kurisu, Yuko Nakamura, Yasutaka Baba, Makoto Iida, Kazuo Awai

ABSTRACT

OBJECTIVES: The objective of this study was to investigate the usefulness of model-based iterative reconstruction (IR) for detecting neointimal formations after carotid artery stenting. METHODS: In a cervical phantom harbouring carotid artery stents, we placed simulated neointimal formations measuring 0.40, 0.60, 0.80 and 1.00 mm along the stent wall. The thickness of in-stent neointimal formations was measured on images reconstructed with filtered-back projection (FBP), hybrid IR (AIDR 3D), and model-based IR (FIRST). The clinical study included 43 patients with carotid stents. Cervical computed tomography (CT) images obtained on a 320-slice scanner were reconstructed with AIDR 3D and FIRST. Five blinded observers visually graded the likelihood of neointimal formations on AIDR 3D and AIDR 3D plus FIRST images. Carotid ultrasound images were the reference standard. We analysed results of visual grading by using a Jack-knife type receiver observer characteristics analysis software. RESULTS: In the phantom study, the difference between the measured and the true diameter of the neointimal formations was smaller on FIRST than FBP or AIDR 3D images. In the clinical study, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of AIDR 3D were 58%, 88%, 83%, 67% and 73%, respectively. For AIDR 3D plus FIRST images they were 84%, 78%, 80%, 82% and 81%, respectively. The mean area under the curve was significantly higher on AIDR 3D plus FIRST than AIDR 3D images (0.82 vs 0.72; p < 0.01). CONCLUSIONS: The model-based IR algorithm helped to improve diagnostic performance for the detection of neointimal formations after carotid artery stenting. KEY POINTS: • Neointimal formations can be visualised more accurately with model-based IR. • Model-based IR improves the detection of neointimal formations after carotid artery stenting. • Model-based IR is suitable for follow up after carotid artery stenting. More... »

PAGES

161-167

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00330-018-5598-5

DOI

http://dx.doi.org/10.1007/s00330-018-5598-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105067640

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29934669


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": "Hiroshima University", 
          "id": "https://www.grid.ac/institutes/grid.257022.0", 
          "name": [
            "Department of Radiology, Hiroshima University Hospital, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan", 
            "Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yokomachi", 
        "givenName": "Kazushi", 
        "id": "sg:person.0621271074.90", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621271074.90"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima University", 
          "id": "https://www.grid.ac/institutes/grid.257022.0", 
          "name": [
            "Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tatsugami", 
        "givenName": "Fuminari", 
        "id": "sg:person.0665050213.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0665050213.43"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima University", 
          "id": "https://www.grid.ac/institutes/grid.257022.0", 
          "name": [
            "Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Higaki", 
        "givenName": "Toru", 
        "id": "sg:person.0701112206.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0701112206.82"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.470097.d", 
          "name": [
            "Department of Radiology, Hiroshima University Hospital, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kume", 
        "givenName": "Shinji", 
        "id": "sg:person.01003317013.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01003317013.03"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.470097.d", 
          "name": [
            "Department of Neurosurgery, Hiroshima University Hospital, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sakamoto", 
        "givenName": "Shigeyuki", 
        "id": "sg:person.01111710621.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01111710621.10"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.470097.d", 
          "name": [
            "Department of Neurosurgery, Hiroshima University Hospital, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Okazaki", 
        "givenName": "Takahito", 
        "id": "sg:person.0655071044.78", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0655071044.78"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.470097.d", 
          "name": [
            "Department of Neurosurgery, Hiroshima University Hospital, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kurisu", 
        "givenName": "Kaoru", 
        "id": "sg:person.0745066302.24", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0745066302.24"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima University", 
          "id": "https://www.grid.ac/institutes/grid.257022.0", 
          "name": [
            "Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nakamura", 
        "givenName": "Yuko", 
        "id": "sg:person.0645742765.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0645742765.43"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima University", 
          "id": "https://www.grid.ac/institutes/grid.257022.0", 
          "name": [
            "Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Baba", 
        "givenName": "Yasutaka", 
        "id": "sg:person.0770021542.71", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0770021542.71"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima University", 
          "id": "https://www.grid.ac/institutes/grid.257022.0", 
          "name": [
            "Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Iida", 
        "givenName": "Makoto", 
        "id": "sg:person.01024153303.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01024153303.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima University", 
          "id": "https://www.grid.ac/institutes/grid.257022.0", 
          "name": [
            "Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Awai", 
        "givenName": "Kazuo", 
        "id": "sg:person.01253621047.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01253621047.18"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.acra.2007.12.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000850042"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ultrasmedbio.2008.11.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001754592"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2015132766", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006750918"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1474-4422(12)70159-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006837247"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa040127", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007305514"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1474-4422(09)70227-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018015985"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejm199108153250701", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021945105"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.110.610212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024184833"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.110.610212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024184833"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.110.610212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024184833"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2011.07.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025685725"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.1995.03520420037035", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026095536"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0140-6736(14)61184-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028229766"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejrad.2012.06.022", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030094177"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.str.25.12.2377", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035431926"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2012.02.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040201139"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.110.589309", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040540680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.110.589309", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040540680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.str.0000152357.82843.9f", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040652328"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.str.0000152357.82843.9f", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040652328"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.273.18.1421", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054158233"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.acra.2016.12.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083824383"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11604-017-0618-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083828721", 
          "https://doi.org/10.1007/s11604-017-0618-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11604-017-0618-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083828721", 
          "https://doi.org/10.1007/s11604-017-0618-y"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-01", 
    "datePublishedReg": "2019-01-01", 
    "description": "OBJECTIVES: The objective of this study was to investigate the usefulness of model-based iterative reconstruction (IR) for detecting neointimal formations after carotid artery stenting.\nMETHODS: In a cervical phantom harbouring carotid artery stents, we placed simulated neointimal formations measuring 0.40, 0.60, 0.80 and 1.00 mm along the stent wall. The thickness of in-stent neointimal formations was measured on images reconstructed with filtered-back projection (FBP), hybrid IR (AIDR 3D), and model-based IR (FIRST). The clinical study included 43 patients with carotid stents. Cervical computed tomography (CT) images obtained on a 320-slice scanner were reconstructed with AIDR 3D and FIRST. Five blinded observers visually graded the likelihood of neointimal formations on AIDR 3D and AIDR 3D plus FIRST images. Carotid ultrasound images were the reference standard. We analysed results of visual grading by using a Jack-knife type receiver observer characteristics analysis software.\nRESULTS: In the phantom study, the difference between the measured and the true diameter of the neointimal formations was smaller on FIRST than FBP or AIDR 3D images. In the clinical study, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of AIDR 3D were 58%, 88%, 83%, 67% and 73%, respectively. For AIDR 3D plus FIRST images they were 84%, 78%, 80%, 82% and 81%, respectively. The mean area under the curve was significantly higher on AIDR 3D plus FIRST than AIDR 3D images (0.82 vs 0.72; p < 0.01).\nCONCLUSIONS: The model-based IR algorithm helped to improve diagnostic performance for the detection of neointimal formations after carotid artery stenting.\nKEY POINTS: \u2022 Neointimal formations can be visualised more accurately with model-based IR. \u2022 Model-based IR improves the detection of neointimal formations after carotid artery stenting. \u2022 Model-based IR is suitable for follow up after carotid artery stenting.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00330-018-5598-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1289120", 
        "issn": [
          "0938-7994", 
          "1432-1084"
        ], 
        "name": "European Radiology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "29"
      }
    ], 
    "name": "Neointimal formation after carotid artery stenting: phantom and clinical evaluation of model-based iterative reconstruction (MBIR)", 
    "pagination": "161-167", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "1d0a9f440242943ef39f0bc484e843b2d3b88c3cf2d3d6511c6fc62ef546d817"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29934669"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9114774"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00330-018-5598-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1105067640"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00330-018-5598-5", 
      "https://app.dimensions.ai/details/publication/pub.1105067640"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:14", 
    "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/v90/0000000353_0000000353/records_45376_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00330-018-5598-5"
  }
]
 

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/s00330-018-5598-5'

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/s00330-018-5598-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00330-018-5598-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00330-018-5598-5'


 

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

202 TRIPLES      21 PREDICATES      48 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00330-018-5598-5 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N996fc4209c7c480a88abc413f442883b
4 schema:citation sg:pub.10.1007/s11604-017-0618-y
5 https://doi.org/10.1001/jama.1995.03520420037035
6 https://doi.org/10.1001/jama.273.18.1421
7 https://doi.org/10.1016/j.acra.2007.12.015
8 https://doi.org/10.1016/j.acra.2016.12.020
9 https://doi.org/10.1016/j.ejrad.2012.06.022
10 https://doi.org/10.1016/j.jcct.2011.07.001
11 https://doi.org/10.1016/j.jcct.2012.02.009
12 https://doi.org/10.1016/j.ultrasmedbio.2008.11.001
13 https://doi.org/10.1016/s0140-6736(14)61184-3
14 https://doi.org/10.1016/s1474-4422(09)70227-3
15 https://doi.org/10.1016/s1474-4422(12)70159-x
16 https://doi.org/10.1056/nejm199108153250701
17 https://doi.org/10.1056/nejmoa040127
18 https://doi.org/10.1148/radiol.2015132766
19 https://doi.org/10.1161/01.str.0000152357.82843.9f
20 https://doi.org/10.1161/01.str.25.12.2377
21 https://doi.org/10.1161/strokeaha.110.589309
22 https://doi.org/10.1161/strokeaha.110.610212
23 schema:datePublished 2019-01
24 schema:datePublishedReg 2019-01-01
25 schema:description OBJECTIVES: The objective of this study was to investigate the usefulness of model-based iterative reconstruction (IR) for detecting neointimal formations after carotid artery stenting. METHODS: In a cervical phantom harbouring carotid artery stents, we placed simulated neointimal formations measuring 0.40, 0.60, 0.80 and 1.00 mm along the stent wall. The thickness of in-stent neointimal formations was measured on images reconstructed with filtered-back projection (FBP), hybrid IR (AIDR 3D), and model-based IR (FIRST). The clinical study included 43 patients with carotid stents. Cervical computed tomography (CT) images obtained on a 320-slice scanner were reconstructed with AIDR 3D and FIRST. Five blinded observers visually graded the likelihood of neointimal formations on AIDR 3D and AIDR 3D plus FIRST images. Carotid ultrasound images were the reference standard. We analysed results of visual grading by using a Jack-knife type receiver observer characteristics analysis software. RESULTS: In the phantom study, the difference between the measured and the true diameter of the neointimal formations was smaller on FIRST than FBP or AIDR 3D images. In the clinical study, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of AIDR 3D were 58%, 88%, 83%, 67% and 73%, respectively. For AIDR 3D plus FIRST images they were 84%, 78%, 80%, 82% and 81%, respectively. The mean area under the curve was significantly higher on AIDR 3D plus FIRST than AIDR 3D images (0.82 vs 0.72; p < 0.01). CONCLUSIONS: The model-based IR algorithm helped to improve diagnostic performance for the detection of neointimal formations after carotid artery stenting. KEY POINTS: • Neointimal formations can be visualised more accurately with model-based IR. • Model-based IR improves the detection of neointimal formations after carotid artery stenting. • Model-based IR is suitable for follow up after carotid artery stenting.
26 schema:genre research_article
27 schema:inLanguage en
28 schema:isAccessibleForFree false
29 schema:isPartOf N47eb9a9374f145d5a180011b44b4a09a
30 Naa53a7dc48604e449b310ef91be64251
31 sg:journal.1289120
32 schema:name Neointimal formation after carotid artery stenting: phantom and clinical evaluation of model-based iterative reconstruction (MBIR)
33 schema:pagination 161-167
34 schema:productId N5c9a546a171d411eab11f136e3a8f096
35 N99f5f9740f4843aeb58d20acc562f308
36 Na8896a8fe6b949cdb142412e5bc05960
37 Nad0472332f6645c48938448506ff670d
38 Nd63ca03e379a472e9a1abd0c13f1afd1
39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105067640
40 https://doi.org/10.1007/s00330-018-5598-5
41 schema:sdDatePublished 2019-04-11T11:14
42 schema:sdLicense https://scigraph.springernature.com/explorer/license/
43 schema:sdPublisher Nb387b33908244064941dacc8de0af0a7
44 schema:url https://link.springer.com/10.1007%2Fs00330-018-5598-5
45 sgo:license sg:explorer/license/
46 sgo:sdDataset articles
47 rdf:type schema:ScholarlyArticle
48 N2bacedec37324664bd724ca81f23fe69 rdf:first sg:person.0645742765.43
49 rdf:rest N4291c8ad765f4603b64b0da5ca8ee5f8
50 N4291c8ad765f4603b64b0da5ca8ee5f8 rdf:first sg:person.0770021542.71
51 rdf:rest N9b769d9caf294d76ac1d1cc34c302932
52 N47eb9a9374f145d5a180011b44b4a09a schema:issueNumber 1
53 rdf:type schema:PublicationIssue
54 N4be379efa1534e7787a1caa60c3a0b7a rdf:first sg:person.01111710621.10
55 rdf:rest Na6463e0fb12f429b8643fd2ee627e375
56 N5c9a546a171d411eab11f136e3a8f096 schema:name readcube_id
57 schema:value 1d0a9f440242943ef39f0bc484e843b2d3b88c3cf2d3d6511c6fc62ef546d817
58 rdf:type schema:PropertyValue
59 N607bb77771fa4bdea4cd85297b5717b5 rdf:first sg:person.0701112206.82
60 rdf:rest Nd1784f1421724bba82a8eae7782f359f
61 N996fc4209c7c480a88abc413f442883b rdf:first sg:person.0621271074.90
62 rdf:rest Nbe4b593938f645eebe78af75946a90b7
63 N99f5f9740f4843aeb58d20acc562f308 schema:name doi
64 schema:value 10.1007/s00330-018-5598-5
65 rdf:type schema:PropertyValue
66 N9b769d9caf294d76ac1d1cc34c302932 rdf:first sg:person.01024153303.19
67 rdf:rest Ncef4474c88884d4aa02a84846e1ae546
68 Na6463e0fb12f429b8643fd2ee627e375 rdf:first sg:person.0655071044.78
69 rdf:rest Nf8daea2fd51447dfa82e273ccec24517
70 Na8896a8fe6b949cdb142412e5bc05960 schema:name dimensions_id
71 schema:value pub.1105067640
72 rdf:type schema:PropertyValue
73 Naa53a7dc48604e449b310ef91be64251 schema:volumeNumber 29
74 rdf:type schema:PublicationVolume
75 Nad0472332f6645c48938448506ff670d schema:name nlm_unique_id
76 schema:value 9114774
77 rdf:type schema:PropertyValue
78 Nb387b33908244064941dacc8de0af0a7 schema:name Springer Nature - SN SciGraph project
79 rdf:type schema:Organization
80 Nbe4b593938f645eebe78af75946a90b7 rdf:first sg:person.0665050213.43
81 rdf:rest N607bb77771fa4bdea4cd85297b5717b5
82 Ncef4474c88884d4aa02a84846e1ae546 rdf:first sg:person.01253621047.18
83 rdf:rest rdf:nil
84 Nd1784f1421724bba82a8eae7782f359f rdf:first sg:person.01003317013.03
85 rdf:rest N4be379efa1534e7787a1caa60c3a0b7a
86 Nd63ca03e379a472e9a1abd0c13f1afd1 schema:name pubmed_id
87 schema:value 29934669
88 rdf:type schema:PropertyValue
89 Nf8daea2fd51447dfa82e273ccec24517 rdf:first sg:person.0745066302.24
90 rdf:rest N2bacedec37324664bd724ca81f23fe69
91 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
92 schema:name Information and Computing Sciences
93 rdf:type schema:DefinedTerm
94 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
95 schema:name Artificial Intelligence and Image Processing
96 rdf:type schema:DefinedTerm
97 sg:journal.1289120 schema:issn 0938-7994
98 1432-1084
99 schema:name European Radiology
100 rdf:type schema:Periodical
101 sg:person.01003317013.03 schema:affiliation https://www.grid.ac/institutes/grid.470097.d
102 schema:familyName Kume
103 schema:givenName Shinji
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01003317013.03
105 rdf:type schema:Person
106 sg:person.01024153303.19 schema:affiliation https://www.grid.ac/institutes/grid.257022.0
107 schema:familyName Iida
108 schema:givenName Makoto
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01024153303.19
110 rdf:type schema:Person
111 sg:person.01111710621.10 schema:affiliation https://www.grid.ac/institutes/grid.470097.d
112 schema:familyName Sakamoto
113 schema:givenName Shigeyuki
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01111710621.10
115 rdf:type schema:Person
116 sg:person.01253621047.18 schema:affiliation https://www.grid.ac/institutes/grid.257022.0
117 schema:familyName Awai
118 schema:givenName Kazuo
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01253621047.18
120 rdf:type schema:Person
121 sg:person.0621271074.90 schema:affiliation https://www.grid.ac/institutes/grid.257022.0
122 schema:familyName Yokomachi
123 schema:givenName Kazushi
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621271074.90
125 rdf:type schema:Person
126 sg:person.0645742765.43 schema:affiliation https://www.grid.ac/institutes/grid.257022.0
127 schema:familyName Nakamura
128 schema:givenName Yuko
129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0645742765.43
130 rdf:type schema:Person
131 sg:person.0655071044.78 schema:affiliation https://www.grid.ac/institutes/grid.470097.d
132 schema:familyName Okazaki
133 schema:givenName Takahito
134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0655071044.78
135 rdf:type schema:Person
136 sg:person.0665050213.43 schema:affiliation https://www.grid.ac/institutes/grid.257022.0
137 schema:familyName Tatsugami
138 schema:givenName Fuminari
139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0665050213.43
140 rdf:type schema:Person
141 sg:person.0701112206.82 schema:affiliation https://www.grid.ac/institutes/grid.257022.0
142 schema:familyName Higaki
143 schema:givenName Toru
144 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0701112206.82
145 rdf:type schema:Person
146 sg:person.0745066302.24 schema:affiliation https://www.grid.ac/institutes/grid.470097.d
147 schema:familyName Kurisu
148 schema:givenName Kaoru
149 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0745066302.24
150 rdf:type schema:Person
151 sg:person.0770021542.71 schema:affiliation https://www.grid.ac/institutes/grid.257022.0
152 schema:familyName Baba
153 schema:givenName Yasutaka
154 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0770021542.71
155 rdf:type schema:Person
156 sg:pub.10.1007/s11604-017-0618-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1083828721
157 https://doi.org/10.1007/s11604-017-0618-y
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1001/jama.1995.03520420037035 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026095536
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1001/jama.273.18.1421 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054158233
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.acra.2007.12.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000850042
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1016/j.acra.2016.12.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083824383
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/j.ejrad.2012.06.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030094177
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1016/j.jcct.2011.07.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025685725
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/j.jcct.2012.02.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040201139
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/j.ultrasmedbio.2008.11.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001754592
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/s0140-6736(14)61184-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028229766
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/s1474-4422(09)70227-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018015985
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1016/s1474-4422(12)70159-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1006837247
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1056/nejm199108153250701 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021945105
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1056/nejmoa040127 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007305514
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1148/radiol.2015132766 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006750918
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1161/01.str.0000152357.82843.9f schema:sameAs https://app.dimensions.ai/details/publication/pub.1040652328
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1161/01.str.25.12.2377 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035431926
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1161/strokeaha.110.589309 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040540680
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1161/strokeaha.110.610212 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024184833
194 rdf:type schema:CreativeWork
195 https://www.grid.ac/institutes/grid.257022.0 schema:alternateName Hiroshima University
196 schema:name Department of Diagnostic Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan
197 Department of Radiology, Hiroshima University Hospital, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan
198 rdf:type schema:Organization
199 https://www.grid.ac/institutes/grid.470097.d schema:alternateName Hiroshima University Hospital
200 schema:name Department of Neurosurgery, Hiroshima University Hospital, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan
201 Department of Radiology, Hiroshima University Hospital, Kasumi 1-2-3, Minami-ku, 734-8551, Hiroshima, Japan
202 rdf:type schema:Organization
 




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


...