Iterative metal artifact reduction: Evaluation and optimization of technique View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-12

AUTHORS

Naveen Subhas, Andrew N. Primak, Nancy A. Obuchowski, Amit Gupta, Joshua M. Polster, Andreas Krauss, Joseph P. Iannotti

ABSTRACT

OBJECTIVE: Iterative metal artifact reduction (IMAR) is a sinogram inpainting technique that incorporates high-frequency data from standard weighted filtered back projection (WFBP) reconstructions to reduce metal artifact on computed tomography (CT). This study was designed to compare the image quality of IMAR and WFBP in total shoulder arthroplasties (TSA); determine the optimal amount of WFBP high-frequency data needed for IMAR; and compare image quality of the standard 3D technique with that of a faster 2D technique. MATERIALS AND METHODS: Eight patients with nine TSA underwent CT with standardized parameters: 140 kVp, 300 mAs, 0.6 mm collimation and slice thickness, and B30 kernel. WFBP, three 3D IMAR algorithms with different amounts of WFBP high-frequency data (IMARlo, lowest; IMARmod, moderate; IMARhi, highest), and one 2D IMAR algorithm were reconstructed. Differences in attenuation near hardware and away from hardware were measured and compared using repeated measures ANOVA. Five readers independently graded image quality; scores were compared using Friedman's test. RESULTS: Attenuation differences were smaller with all 3D IMAR techniques than with WFBP (p < 0.0063). With increasing high-frequency data, the attenuation difference increased slightly (differences not statistically significant). All readers ranked IMARmod and IMARhi more favorably than WFBP (p < 0.05), with IMARmod ranked highest for most structures. The attenuation difference was slightly higher with 2D than with 3D IMAR, with no significant reader preference for 3D over 2D. CONCLUSIONS: IMAR significantly decreases metal artifact compared to WFBP both objectively and subjectively in TSA. The incorporation of a moderate amount of WFBP high-frequency data and use of a 2D reconstruction technique optimize image quality and allow for relatively short reconstruction times. More... »

PAGES

1729-1735

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00256-014-1987-2

DOI

http://dx.doi.org/10.1007/s00256-014-1987-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008640892

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25172218


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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Analysis of Variance", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Arthroplasty", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Artifacts", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Imaging, Three-Dimensional", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Metals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiographic Image Interpretation, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sensitivity and Specificity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Shoulder Joint", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, X-Ray Computed", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Cleveland Clinic", 
          "id": "https://www.grid.ac/institutes/grid.239578.2", 
          "name": [
            "Cleveland Clinic, Imaging Institute, 9500 Euclid Ave, A21, 44195, Cleveland, OH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Subhas", 
        "givenName": "Naveen", 
        "id": "sg:person.01160444271.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160444271.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Siemens (United States)", 
          "id": "https://www.grid.ac/institutes/grid.419233.e", 
          "name": [
            "Siemens Medical Solutions USA Inc., 19355, Malvern, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Primak", 
        "givenName": "Andrew N.", 
        "id": "sg:person.010520761047.93", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010520761047.93"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cleveland Clinic", 
          "id": "https://www.grid.ac/institutes/grid.239578.2", 
          "name": [
            "Quantitative Health Sciences, Cleveland Clinic, 44195, Cleveland, OH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Obuchowski", 
        "givenName": "Nancy A.", 
        "id": "sg:person.0624614663.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0624614663.59"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cleveland Clinic", 
          "id": "https://www.grid.ac/institutes/grid.239578.2", 
          "name": [
            "Cleveland Clinic, Imaging Institute, 9500 Euclid Ave, A21, 44195, Cleveland, OH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gupta", 
        "givenName": "Amit", 
        "id": "sg:person.01213554412.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213554412.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cleveland Clinic", 
          "id": "https://www.grid.ac/institutes/grid.239578.2", 
          "name": [
            "Cleveland Clinic, Imaging Institute, 9500 Euclid Ave, A21, 44195, Cleveland, OH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Polster", 
        "givenName": "Joshua M.", 
        "id": "sg:person.0710775110.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0710775110.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Siemens (Germany)", 
          "id": "https://www.grid.ac/institutes/grid.5406.7", 
          "name": [
            "Siemens Healthcare, Forchheim, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Krauss", 
        "givenName": "Andreas", 
        "id": "sg:person.0724777566.64", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0724777566.64"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cleveland Clinic", 
          "id": "https://www.grid.ac/institutes/grid.239578.2", 
          "name": [
            "Cleveland Clinic, Orthopaedic & Rheumatologic Institute, 44195, Cleveland, OH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Iannotti", 
        "givenName": "Joseph P.", 
        "id": "sg:person.01243422032.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01243422032.83"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1097/rli.0b013e3182532f17", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008428455"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rli.0b013e3182532f17", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008428455"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1118/1.3484090", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010203054"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1118/1.3691902", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041594040"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.13122089", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041919942"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-011-2062-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045347713", 
          "https://doi.org/10.1007/s00330-011-2062-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-013-2885-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048773651", 
          "https://doi.org/10.1007/s00330-013-2885-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.164.2.3602406", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079845244"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-12", 
    "datePublishedReg": "2014-12-01", 
    "description": "OBJECTIVE: Iterative metal artifact reduction (IMAR) is a sinogram inpainting technique that incorporates high-frequency data from standard weighted filtered back projection (WFBP) reconstructions to reduce metal artifact on computed tomography (CT). This study was designed to compare the image quality of IMAR and WFBP in total shoulder arthroplasties (TSA); determine the optimal amount of WFBP high-frequency data needed for IMAR; and compare image quality of the standard 3D technique with that of a faster 2D technique.\nMATERIALS AND METHODS: Eight patients with nine TSA underwent CT with standardized parameters: 140 kVp, 300 mAs, 0.6\u00a0mm collimation and slice thickness, and B30 kernel. WFBP, three 3D IMAR algorithms with different amounts of WFBP high-frequency data (IMARlo, lowest; IMARmod, moderate; IMARhi, highest), and one 2D IMAR algorithm were reconstructed. Differences in attenuation near hardware and away from hardware were measured and compared using repeated measures ANOVA. Five readers independently graded image quality; scores were compared using Friedman's test.\nRESULTS: Attenuation differences were smaller with all 3D IMAR techniques than with WFBP (p\u2009<\u20090.0063). With increasing high-frequency data, the attenuation difference increased slightly (differences not statistically significant). All readers ranked IMARmod and IMARhi more favorably than WFBP (p\u2009<\u20090.05), with IMARmod ranked highest for most structures. The attenuation difference was slightly higher with 2D than with 3D IMAR, with no significant reader preference for 3D over 2D.\nCONCLUSIONS: IMAR significantly decreases metal artifact compared to WFBP both objectively and subjectively in TSA. The incorporation of a moderate amount of WFBP high-frequency data and use of a 2D reconstruction technique optimize image quality and allow for relatively short reconstruction times.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00256-014-1987-2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1086201", 
        "issn": [
          "0364-2348", 
          "1432-2161"
        ], 
        "name": "Skeletal Radiology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "12", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "43"
      }
    ], 
    "name": "Iterative metal artifact reduction: Evaluation and optimization of technique", 
    "pagination": "1729-1735", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "292af15bde3432c85b3633f12eec48495aa71b01f2f247b9e3d4e759f3c77fb2"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "25172218"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "7701953"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00256-014-1987-2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1008640892"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00256-014-1987-2", 
      "https://app.dimensions.ai/details/publication/pub.1008640892"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T23:17", 
    "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_8693_00000480.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s00256-014-1987-2"
  }
]
 

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/s00256-014-1987-2'

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/s00256-014-1987-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00256-014-1987-2'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00256-014-1987-2'


 

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

210 TRIPLES      21 PREDICATES      53 URIs      38 LITERALS      26 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00256-014-1987-2 schema:about N0a6761d9554a4341a1da255a1c03a4e1
2 N300e933f6aba473aa8f51f7cd84726f0
3 N3bd962cbc0cb4b408d4e3764d93241f4
4 N44203e77178f48ffb4d676c1f6862e85
5 N644f9e1aa95143beb3e931cd0fd28107
6 N6a58cbdd871d4b0fa9c368abd550b664
7 N6bd36417b24545b9a2d3a448b7a1f103
8 N6d367a10f3304c7f8a3dfaaeef5fe6b1
9 N75e0a8db9c9b46f2ba988ddd49211bd1
10 N7dd69ac06d574adebbb480454f3c439f
11 N7e4eac8f14ff445b9daf76b429c0679b
12 N8353fbe4b8c34d8d9bfd9a301f3ebd63
13 N94e3022c4aff4c599ef8dbb1e35ac72b
14 N99684c877ae44a099c64ce04b0d75a96
15 Na4a675bd5da447709fee01fe9bb0453f
16 Ne68a0398fc1748ca8a620700137b86e9
17 Nf99a7716c85740e1a2c31bb9192c072e
18 anzsrc-for:08
19 anzsrc-for:0801
20 schema:author Ne7582df08de645eda837fe1b6dc2c82f
21 schema:citation sg:pub.10.1007/s00330-011-2062-1
22 sg:pub.10.1007/s00330-013-2885-z
23 https://doi.org/10.1097/rli.0b013e3182532f17
24 https://doi.org/10.1118/1.3484090
25 https://doi.org/10.1118/1.3691902
26 https://doi.org/10.1148/radiol.13122089
27 https://doi.org/10.1148/radiology.164.2.3602406
28 schema:datePublished 2014-12
29 schema:datePublishedReg 2014-12-01
30 schema:description OBJECTIVE: Iterative metal artifact reduction (IMAR) is a sinogram inpainting technique that incorporates high-frequency data from standard weighted filtered back projection (WFBP) reconstructions to reduce metal artifact on computed tomography (CT). This study was designed to compare the image quality of IMAR and WFBP in total shoulder arthroplasties (TSA); determine the optimal amount of WFBP high-frequency data needed for IMAR; and compare image quality of the standard 3D technique with that of a faster 2D technique. MATERIALS AND METHODS: Eight patients with nine TSA underwent CT with standardized parameters: 140 kVp, 300 mAs, 0.6 mm collimation and slice thickness, and B30 kernel. WFBP, three 3D IMAR algorithms with different amounts of WFBP high-frequency data (IMARlo, lowest; IMARmod, moderate; IMARhi, highest), and one 2D IMAR algorithm were reconstructed. Differences in attenuation near hardware and away from hardware were measured and compared using repeated measures ANOVA. Five readers independently graded image quality; scores were compared using Friedman's test. RESULTS: Attenuation differences were smaller with all 3D IMAR techniques than with WFBP (p < 0.0063). With increasing high-frequency data, the attenuation difference increased slightly (differences not statistically significant). All readers ranked IMARmod and IMARhi more favorably than WFBP (p < 0.05), with IMARmod ranked highest for most structures. The attenuation difference was slightly higher with 2D than with 3D IMAR, with no significant reader preference for 3D over 2D. CONCLUSIONS: IMAR significantly decreases metal artifact compared to WFBP both objectively and subjectively in TSA. The incorporation of a moderate amount of WFBP high-frequency data and use of a 2D reconstruction technique optimize image quality and allow for relatively short reconstruction times.
31 schema:genre research_article
32 schema:inLanguage en
33 schema:isAccessibleForFree false
34 schema:isPartOf N0cfcbe9fbe0f45a98b133df5204809cd
35 N54688df6d4cd4cb38b0da5e1381529d1
36 sg:journal.1086201
37 schema:name Iterative metal artifact reduction: Evaluation and optimization of technique
38 schema:pagination 1729-1735
39 schema:productId N1ff302fdf7cc4bdebe945309d23a662e
40 Nba873e5c019e42e6a5f46781ea4a33c3
41 Nd3ca2141f55b468b929814f0fda5f8f0
42 Nde3b5e3faa4b4733b9a11c19e21babc8
43 Nfbf57c9ef1f14fbfa638e88d94251506
44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008640892
45 https://doi.org/10.1007/s00256-014-1987-2
46 schema:sdDatePublished 2019-04-10T23:17
47 schema:sdLicense https://scigraph.springernature.com/explorer/license/
48 schema:sdPublisher Nb523dfe5dd2942ac838a538946a1accb
49 schema:url http://link.springer.com/10.1007/s00256-014-1987-2
50 sgo:license sg:explorer/license/
51 sgo:sdDataset articles
52 rdf:type schema:ScholarlyArticle
53 N0066e8859f0c4420909307d487ab841e rdf:first sg:person.0710775110.08
54 rdf:rest N37a297e02f3d45d39215a96b3861db55
55 N0a6761d9554a4341a1da255a1c03a4e1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
56 schema:name Aged
57 rdf:type schema:DefinedTerm
58 N0cfcbe9fbe0f45a98b133df5204809cd schema:volumeNumber 43
59 rdf:type schema:PublicationVolume
60 N0e8ca05e562647cd9f1ca9440f646f9c rdf:first sg:person.010520761047.93
61 rdf:rest Na5687f2c22354631bf86a34c2c118605
62 N1ff302fdf7cc4bdebe945309d23a662e schema:name dimensions_id
63 schema:value pub.1008640892
64 rdf:type schema:PropertyValue
65 N300e933f6aba473aa8f51f7cd84726f0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
66 schema:name Artifacts
67 rdf:type schema:DefinedTerm
68 N37a297e02f3d45d39215a96b3861db55 rdf:first sg:person.0724777566.64
69 rdf:rest Nec74a6fa527d4ff8a52e8ac6c57c20ad
70 N3bd962cbc0cb4b408d4e3764d93241f4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
71 schema:name Sensitivity and Specificity
72 rdf:type schema:DefinedTerm
73 N44203e77178f48ffb4d676c1f6862e85 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
74 schema:name Male
75 rdf:type schema:DefinedTerm
76 N54688df6d4cd4cb38b0da5e1381529d1 schema:issueNumber 12
77 rdf:type schema:PublicationIssue
78 N644f9e1aa95143beb3e931cd0fd28107 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
79 schema:name Tomography, X-Ray Computed
80 rdf:type schema:DefinedTerm
81 N6a58cbdd871d4b0fa9c368abd550b664 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
82 schema:name Metals
83 rdf:type schema:DefinedTerm
84 N6bd36417b24545b9a2d3a448b7a1f103 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
85 schema:name Arthroplasty
86 rdf:type schema:DefinedTerm
87 N6d367a10f3304c7f8a3dfaaeef5fe6b1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
88 schema:name Humans
89 rdf:type schema:DefinedTerm
90 N75e0a8db9c9b46f2ba988ddd49211bd1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
91 schema:name Reproducibility of Results
92 rdf:type schema:DefinedTerm
93 N7dd69ac06d574adebbb480454f3c439f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
94 schema:name Middle Aged
95 rdf:type schema:DefinedTerm
96 N7e4eac8f14ff445b9daf76b429c0679b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Algorithms
98 rdf:type schema:DefinedTerm
99 N8353fbe4b8c34d8d9bfd9a301f3ebd63 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
100 schema:name Imaging, Three-Dimensional
101 rdf:type schema:DefinedTerm
102 N8b2ac79f26da4cdb992fdca538ed085d rdf:first sg:person.01213554412.32
103 rdf:rest N0066e8859f0c4420909307d487ab841e
104 N94e3022c4aff4c599ef8dbb1e35ac72b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
105 schema:name Analysis of Variance
106 rdf:type schema:DefinedTerm
107 N99684c877ae44a099c64ce04b0d75a96 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
108 schema:name Radiographic Image Interpretation, Computer-Assisted
109 rdf:type schema:DefinedTerm
110 Na4a675bd5da447709fee01fe9bb0453f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
111 schema:name Female
112 rdf:type schema:DefinedTerm
113 Na5687f2c22354631bf86a34c2c118605 rdf:first sg:person.0624614663.59
114 rdf:rest N8b2ac79f26da4cdb992fdca538ed085d
115 Nb523dfe5dd2942ac838a538946a1accb schema:name Springer Nature - SN SciGraph project
116 rdf:type schema:Organization
117 Nba873e5c019e42e6a5f46781ea4a33c3 schema:name nlm_unique_id
118 schema:value 7701953
119 rdf:type schema:PropertyValue
120 Nd3ca2141f55b468b929814f0fda5f8f0 schema:name doi
121 schema:value 10.1007/s00256-014-1987-2
122 rdf:type schema:PropertyValue
123 Nde3b5e3faa4b4733b9a11c19e21babc8 schema:name readcube_id
124 schema:value 292af15bde3432c85b3633f12eec48495aa71b01f2f247b9e3d4e759f3c77fb2
125 rdf:type schema:PropertyValue
126 Ne68a0398fc1748ca8a620700137b86e9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
127 schema:name Adult
128 rdf:type schema:DefinedTerm
129 Ne7582df08de645eda837fe1b6dc2c82f rdf:first sg:person.01160444271.39
130 rdf:rest N0e8ca05e562647cd9f1ca9440f646f9c
131 Nec74a6fa527d4ff8a52e8ac6c57c20ad rdf:first sg:person.01243422032.83
132 rdf:rest rdf:nil
133 Nf99a7716c85740e1a2c31bb9192c072e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Shoulder Joint
135 rdf:type schema:DefinedTerm
136 Nfbf57c9ef1f14fbfa638e88d94251506 schema:name pubmed_id
137 schema:value 25172218
138 rdf:type schema:PropertyValue
139 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
140 schema:name Information and Computing Sciences
141 rdf:type schema:DefinedTerm
142 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
143 schema:name Artificial Intelligence and Image Processing
144 rdf:type schema:DefinedTerm
145 sg:journal.1086201 schema:issn 0364-2348
146 1432-2161
147 schema:name Skeletal Radiology
148 rdf:type schema:Periodical
149 sg:person.010520761047.93 schema:affiliation https://www.grid.ac/institutes/grid.419233.e
150 schema:familyName Primak
151 schema:givenName Andrew N.
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010520761047.93
153 rdf:type schema:Person
154 sg:person.01160444271.39 schema:affiliation https://www.grid.ac/institutes/grid.239578.2
155 schema:familyName Subhas
156 schema:givenName Naveen
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01160444271.39
158 rdf:type schema:Person
159 sg:person.01213554412.32 schema:affiliation https://www.grid.ac/institutes/grid.239578.2
160 schema:familyName Gupta
161 schema:givenName Amit
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213554412.32
163 rdf:type schema:Person
164 sg:person.01243422032.83 schema:affiliation https://www.grid.ac/institutes/grid.239578.2
165 schema:familyName Iannotti
166 schema:givenName Joseph P.
167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01243422032.83
168 rdf:type schema:Person
169 sg:person.0624614663.59 schema:affiliation https://www.grid.ac/institutes/grid.239578.2
170 schema:familyName Obuchowski
171 schema:givenName Nancy A.
172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0624614663.59
173 rdf:type schema:Person
174 sg:person.0710775110.08 schema:affiliation https://www.grid.ac/institutes/grid.239578.2
175 schema:familyName Polster
176 schema:givenName Joshua M.
177 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0710775110.08
178 rdf:type schema:Person
179 sg:person.0724777566.64 schema:affiliation https://www.grid.ac/institutes/grid.5406.7
180 schema:familyName Krauss
181 schema:givenName Andreas
182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0724777566.64
183 rdf:type schema:Person
184 sg:pub.10.1007/s00330-011-2062-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045347713
185 https://doi.org/10.1007/s00330-011-2062-1
186 rdf:type schema:CreativeWork
187 sg:pub.10.1007/s00330-013-2885-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1048773651
188 https://doi.org/10.1007/s00330-013-2885-z
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1097/rli.0b013e3182532f17 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008428455
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1118/1.3484090 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010203054
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1118/1.3691902 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041594040
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1148/radiol.13122089 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041919942
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1148/radiology.164.2.3602406 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079845244
199 rdf:type schema:CreativeWork
200 https://www.grid.ac/institutes/grid.239578.2 schema:alternateName Cleveland Clinic
201 schema:name Cleveland Clinic, Imaging Institute, 9500 Euclid Ave, A21, 44195, Cleveland, OH, USA
202 Cleveland Clinic, Orthopaedic & Rheumatologic Institute, 44195, Cleveland, OH, USA
203 Quantitative Health Sciences, Cleveland Clinic, 44195, Cleveland, OH, USA
204 rdf:type schema:Organization
205 https://www.grid.ac/institutes/grid.419233.e schema:alternateName Siemens (United States)
206 schema:name Siemens Medical Solutions USA Inc., 19355, Malvern, PA, USA
207 rdf:type schema:Organization
208 https://www.grid.ac/institutes/grid.5406.7 schema:alternateName Siemens (Germany)
209 schema:name Siemens Healthcare, Forchheim, Germany
210 rdf:type schema:Organization
 




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


...