Regional infarction identification from cardiac CT images: a computer-aided biomechanical approach View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-09

AUTHORS

Ken C. L. Wong, Michael Tee, Marcus Chen, David A. Bluemke, Ronald M. Summers, Jianhua Yao

ABSTRACT

PURPOSE: Regional infarction identification is important for heart disease diagnosis and management, and myocardial deformation has been shown to be effective for this purpose. Although tagged and strain-encoded MR images can provide such measurements, they are uncommon in clinical routine. On the contrary, cardiac CT images are more available with lower costs, but they only provide motion of cardiac boundaries and additional constraints are required to obtain the myocardial strains. The goal of this study is to verify the potential of contrast-enhanced CT images on computer-aided regional infarction identification. METHODS: We propose a biomechanical approach combined with machine learning algorithms. A hyperelastic biomechanical model is used with deformable image registration to estimate 3D myocardial strains from CT images. The regional strains and CT image intensities are input to a classifier for regional infarction identification. Cross-validations on ten canine image sequences with artificially induced infarctions were used to study the performances of using different feature combinations and machine learning algorithms. RESULTS: Radial strain, circumferential strain, first principal strain, and image intensity were shown to be discriminative features. The highest identification accuracy ([Formula: see text] %) was achieved when combining radial strain with image intensity. Random forests gave better results than support vector machines on less discriminative features. Random forests also performed better when all strains were used together. CONCLUSION: Although CT images cannot directly measure myocardial deformation, with the use of a biomechanical model, the estimated strains can provide promising identification results especially when combined with CT image intensity. More... »

PAGES

1573-1583

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11548-016-1404-5

DOI

http://dx.doi.org/10.1007/s11548-016-1404-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1047006247

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27072840


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": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Image Enhancement", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Myocardial Infarction", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, X-Ray Computed", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wong", 
        "givenName": "Ken C. L.", 
        "id": "sg:person.01106723443.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01106723443.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Oxford", 
          "id": "https://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA", 
            "Institute of Biomedical Engineering, University of Oxford, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tee", 
        "givenName": "Michael", 
        "id": "sg:person.0776772602.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0776772602.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Cardiovascular and Pulmonary Branch, NHLBI, NIH, Bethesda, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Marcus", 
        "id": "sg:person.0701232757.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0701232757.03"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bluemke", 
        "givenName": "David A.", 
        "id": "sg:person.01121513432.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01121513432.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Summers", 
        "givenName": "Ronald M.", 
        "id": "sg:person.011331054577.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011331054577.30"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yao", 
        "givenName": "Jianhua", 
        "id": "sg:person.012366760067.46", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012366760067.46"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1161/circulationaha.105.521450", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000306762"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0735-1097(00)01186-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001762979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2313030132", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007333491"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1961189.1961199", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013637525"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.media.2012.11.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017989915"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11886-009-0075-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020440290", 
          "https://doi.org/10.1007/s11886-009-0075-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11886-009-0075-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020440290", 
          "https://doi.org/10.1007/s11886-009-0075-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.108.789032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023300241"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.108.789032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023300241"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2012.08.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024243590"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1010933404324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024739340", 
          "https://doi.org/10.1023/a:1010933404324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00994018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025150743", 
          "https://doi.org/10.1007/bf00994018"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-35488-8_13", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026061992", 
          "https://doi.org/10.1007/978-3-540-35488-8_13"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.rcl.2008.10.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028031824"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/cir.0000000000000152", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028161404"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/cir.0000000000000152", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028161404"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compbiomed.2015.03.033", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028746195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.media.2005.01.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032778330"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.110.959817", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039112077"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.110.959817", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039112077"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/hc0402.102975", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039853393"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-005-0057-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040014494", 
          "https://doi.org/10.1007/s00330-005-0057-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-005-0057-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040014494", 
          "https://doi.org/10.1007/s00330-005-0057-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.1910380517", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043139910"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.0000089041.69175.9d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050836677"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-24571-3_18", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052617218", 
          "https://doi.org/10.1007/978-3-319-24571-3_18"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejm200004203421603", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053221725"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2003.815867", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061694447"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2011.2105274", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061695688"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2011.2156805", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061695754"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2011.2171706", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061695817"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/isbi.2009.5193259", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093593486"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-09", 
    "datePublishedReg": "2016-09-01", 
    "description": "PURPOSE: Regional infarction identification is important for heart disease diagnosis and management, and myocardial deformation has been shown to be effective for this purpose. Although tagged and strain-encoded MR images can provide such measurements, they are uncommon in clinical routine. On the contrary, cardiac CT images are more available with lower costs, but they only provide motion of cardiac boundaries and additional constraints are required to obtain the myocardial strains. The goal of this study is to verify the potential of contrast-enhanced CT images on computer-aided regional infarction identification.\nMETHODS: We propose a biomechanical approach combined with machine learning algorithms. A hyperelastic biomechanical model is used with deformable image registration to estimate 3D myocardial strains from CT images. The regional strains and CT image intensities are input to a classifier for regional infarction identification. Cross-validations on ten canine image sequences with artificially induced infarctions were used to study the performances of using different feature combinations and machine learning algorithms.\nRESULTS: Radial strain, circumferential strain, first principal strain, and image intensity were shown to be discriminative features. The highest identification accuracy ([Formula: see text]\u00a0%) was achieved when combining radial strain with image intensity. Random forests gave better results than support vector machines on less discriminative features. Random forests also performed better when all strains were used together.\nCONCLUSION: Although CT images cannot directly measure myocardial deformation, with the use of a biomechanical model, the estimated strains can provide promising identification results especially when combined with CT image intensity.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11548-016-1404-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.4055703", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2725154", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1041191", 
        "issn": [
          "1861-6410", 
          "1861-6429"
        ], 
        "name": "International Journal of Computer Assisted Radiology and Surgery", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "9", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "11"
      }
    ], 
    "name": "Regional infarction identification from cardiac CT images: a computer-aided biomechanical approach", 
    "pagination": "1573-1583", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "dfec3f6d8423a3f22647d1f996e1dc0375d0ee9250dc6d303ed49c48e496a1fb"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "27072840"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101499225"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11548-016-1404-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1047006247"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11548-016-1404-5", 
      "https://app.dimensions.ai/details/publication/pub.1047006247"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:51", 
    "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/v90/0000000371_0000000371/records_130801_00000004.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11548-016-1404-5"
  }
]
 

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/s11548-016-1404-5'

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/s11548-016-1404-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11548-016-1404-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11548-016-1404-5'


 

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

230 TRIPLES      21 PREDICATES      62 URIs      27 LITERALS      15 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11548-016-1404-5 schema:about N02f8afb4befa4495be6508c4e34f0afe
2 N1e06f41ae2e64e26ab668d2d4b879302
3 N44800eb0945647c89f8c3b465d5d2bb6
4 N4b944a69aa0f4690b06440e2767b4492
5 N5ab5575449ed4eaabb7b85bd52f63e2a
6 Ne7468d512aed4be6a34ffea5aad0be21
7 anzsrc-for:08
8 anzsrc-for:0801
9 schema:author N89345bd302d140449d0ee148048334b3
10 schema:citation sg:pub.10.1007/978-3-319-24571-3_18
11 sg:pub.10.1007/978-3-540-35488-8_13
12 sg:pub.10.1007/bf00994018
13 sg:pub.10.1007/s00330-005-0057-5
14 sg:pub.10.1007/s11886-009-0075-z
15 sg:pub.10.1023/a:1010933404324
16 https://doi.org/10.1002/mrm.1910380517
17 https://doi.org/10.1016/j.compbiomed.2015.03.033
18 https://doi.org/10.1016/j.jacc.2012.08.001
19 https://doi.org/10.1016/j.media.2005.01.003
20 https://doi.org/10.1016/j.media.2012.11.007
21 https://doi.org/10.1016/j.rcl.2008.10.006
22 https://doi.org/10.1016/s0735-1097(00)01186-4
23 https://doi.org/10.1056/nejm200004203421603
24 https://doi.org/10.1109/isbi.2009.5193259
25 https://doi.org/10.1109/tmi.2003.815867
26 https://doi.org/10.1109/tmi.2011.2105274
27 https://doi.org/10.1109/tmi.2011.2156805
28 https://doi.org/10.1109/tmi.2011.2171706
29 https://doi.org/10.1145/1961189.1961199
30 https://doi.org/10.1148/radiol.2313030132
31 https://doi.org/10.1161/01.cir.0000089041.69175.9d
32 https://doi.org/10.1161/cir.0000000000000152
33 https://doi.org/10.1161/circimaging.108.789032
34 https://doi.org/10.1161/circimaging.110.959817
35 https://doi.org/10.1161/circulationaha.105.521450
36 https://doi.org/10.1161/hc0402.102975
37 schema:datePublished 2016-09
38 schema:datePublishedReg 2016-09-01
39 schema:description PURPOSE: Regional infarction identification is important for heart disease diagnosis and management, and myocardial deformation has been shown to be effective for this purpose. Although tagged and strain-encoded MR images can provide such measurements, they are uncommon in clinical routine. On the contrary, cardiac CT images are more available with lower costs, but they only provide motion of cardiac boundaries and additional constraints are required to obtain the myocardial strains. The goal of this study is to verify the potential of contrast-enhanced CT images on computer-aided regional infarction identification. METHODS: We propose a biomechanical approach combined with machine learning algorithms. A hyperelastic biomechanical model is used with deformable image registration to estimate 3D myocardial strains from CT images. The regional strains and CT image intensities are input to a classifier for regional infarction identification. Cross-validations on ten canine image sequences with artificially induced infarctions were used to study the performances of using different feature combinations and machine learning algorithms. RESULTS: Radial strain, circumferential strain, first principal strain, and image intensity were shown to be discriminative features. The highest identification accuracy ([Formula: see text] %) was achieved when combining radial strain with image intensity. Random forests gave better results than support vector machines on less discriminative features. Random forests also performed better when all strains were used together. CONCLUSION: Although CT images cannot directly measure myocardial deformation, with the use of a biomechanical model, the estimated strains can provide promising identification results especially when combined with CT image intensity.
40 schema:genre research_article
41 schema:inLanguage en
42 schema:isAccessibleForFree true
43 schema:isPartOf Nefcf33ce0b4342599364d8196654093a
44 Nfa641ed8a2ba4ad3ab0885ce8d847c4a
45 sg:journal.1041191
46 schema:name Regional infarction identification from cardiac CT images: a computer-aided biomechanical approach
47 schema:pagination 1573-1583
48 schema:productId N72833637869b41a8b98f70ca6702774a
49 N78f96815953d4790bf85718913413e07
50 N7f74697ea5084509baed2fd4f8fce0f8
51 N88b66fad9ad143cd9a18277a6779ba5c
52 Nc4b01c311f8f492e863d403c1cc07e82
53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047006247
54 https://doi.org/10.1007/s11548-016-1404-5
55 schema:sdDatePublished 2019-04-11T13:51
56 schema:sdLicense https://scigraph.springernature.com/explorer/license/
57 schema:sdPublisher N27df399a4d784f06bfef7d4ef031c03d
58 schema:url http://link.springer.com/10.1007%2Fs11548-016-1404-5
59 sgo:license sg:explorer/license/
60 sgo:sdDataset articles
61 rdf:type schema:ScholarlyArticle
62 N02f8afb4befa4495be6508c4e34f0afe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
63 schema:name Image Enhancement
64 rdf:type schema:DefinedTerm
65 N046f31926cf743109faff8bca06e664a rdf:first sg:person.011331054577.30
66 rdf:rest N808af90c6e784fd4849c7860947011c4
67 N1e06f41ae2e64e26ab668d2d4b879302 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
68 schema:name Algorithms
69 rdf:type schema:DefinedTerm
70 N224b00bf43bc44549a09ab551f9218d4 schema:name Cardiovascular and Pulmonary Branch, NHLBI, NIH, Bethesda, MD, USA
71 rdf:type schema:Organization
72 N27df399a4d784f06bfef7d4ef031c03d schema:name Springer Nature - SN SciGraph project
73 rdf:type schema:Organization
74 N44800eb0945647c89f8c3b465d5d2bb6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
75 schema:name Myocardial Infarction
76 rdf:type schema:DefinedTerm
77 N4b944a69aa0f4690b06440e2767b4492 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
78 schema:name Tomography, X-Ray Computed
79 rdf:type schema:DefinedTerm
80 N58cc0cd7c5644ba2a6314cbc39f09d3d schema:name Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA
81 rdf:type schema:Organization
82 N5ab5575449ed4eaabb7b85bd52f63e2a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
83 schema:name Humans
84 rdf:type schema:DefinedTerm
85 N71eff4d8948b4c3db64b50b3ebdb46c0 rdf:first sg:person.0776772602.31
86 rdf:rest Na4a48c8f90254ba7a1381a6c8fdc517e
87 N72833637869b41a8b98f70ca6702774a schema:name doi
88 schema:value 10.1007/s11548-016-1404-5
89 rdf:type schema:PropertyValue
90 N78f96815953d4790bf85718913413e07 schema:name pubmed_id
91 schema:value 27072840
92 rdf:type schema:PropertyValue
93 N79b74fd8d886420dab7e5bf145b7d448 schema:name Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA
94 rdf:type schema:Organization
95 N7f74697ea5084509baed2fd4f8fce0f8 schema:name dimensions_id
96 schema:value pub.1047006247
97 rdf:type schema:PropertyValue
98 N808af90c6e784fd4849c7860947011c4 rdf:first sg:person.012366760067.46
99 rdf:rest rdf:nil
100 N88b66fad9ad143cd9a18277a6779ba5c schema:name nlm_unique_id
101 schema:value 101499225
102 rdf:type schema:PropertyValue
103 N89345bd302d140449d0ee148048334b3 rdf:first sg:person.01106723443.05
104 rdf:rest N71eff4d8948b4c3db64b50b3ebdb46c0
105 Na4a48c8f90254ba7a1381a6c8fdc517e rdf:first sg:person.0701232757.03
106 rdf:rest Nb007ac8bc0884447a840d5b56a8a9a4d
107 Na866f72a0837415a99b1eb4a83d33672 schema:name Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA
108 rdf:type schema:Organization
109 Nb007ac8bc0884447a840d5b56a8a9a4d rdf:first sg:person.01121513432.05
110 rdf:rest N046f31926cf743109faff8bca06e664a
111 Nc4b01c311f8f492e863d403c1cc07e82 schema:name readcube_id
112 schema:value dfec3f6d8423a3f22647d1f996e1dc0375d0ee9250dc6d303ed49c48e496a1fb
113 rdf:type schema:PropertyValue
114 Ne7468d512aed4be6a34ffea5aad0be21 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
115 schema:name Reproducibility of Results
116 rdf:type schema:DefinedTerm
117 Nefcf33ce0b4342599364d8196654093a schema:volumeNumber 11
118 rdf:type schema:PublicationVolume
119 Nf89bd46ccc474421adc49cd8dd0d7109 schema:name Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA
120 rdf:type schema:Organization
121 Nfa641ed8a2ba4ad3ab0885ce8d847c4a schema:issueNumber 9
122 rdf:type schema:PublicationIssue
123 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
124 schema:name Information and Computing Sciences
125 rdf:type schema:DefinedTerm
126 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
127 schema:name Artificial Intelligence and Image Processing
128 rdf:type schema:DefinedTerm
129 sg:grant.2725154 http://pending.schema.org/fundedItem sg:pub.10.1007/s11548-016-1404-5
130 rdf:type schema:MonetaryGrant
131 sg:grant.4055703 http://pending.schema.org/fundedItem sg:pub.10.1007/s11548-016-1404-5
132 rdf:type schema:MonetaryGrant
133 sg:journal.1041191 schema:issn 1861-6410
134 1861-6429
135 schema:name International Journal of Computer Assisted Radiology and Surgery
136 rdf:type schema:Periodical
137 sg:person.01106723443.05 schema:affiliation N79b74fd8d886420dab7e5bf145b7d448
138 schema:familyName Wong
139 schema:givenName Ken C. L.
140 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01106723443.05
141 rdf:type schema:Person
142 sg:person.01121513432.05 schema:affiliation Na866f72a0837415a99b1eb4a83d33672
143 schema:familyName Bluemke
144 schema:givenName David A.
145 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01121513432.05
146 rdf:type schema:Person
147 sg:person.011331054577.30 schema:affiliation Nf89bd46ccc474421adc49cd8dd0d7109
148 schema:familyName Summers
149 schema:givenName Ronald M.
150 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011331054577.30
151 rdf:type schema:Person
152 sg:person.012366760067.46 schema:affiliation N58cc0cd7c5644ba2a6314cbc39f09d3d
153 schema:familyName Yao
154 schema:givenName Jianhua
155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012366760067.46
156 rdf:type schema:Person
157 sg:person.0701232757.03 schema:affiliation N224b00bf43bc44549a09ab551f9218d4
158 schema:familyName Chen
159 schema:givenName Marcus
160 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0701232757.03
161 rdf:type schema:Person
162 sg:person.0776772602.31 schema:affiliation https://www.grid.ac/institutes/grid.4991.5
163 schema:familyName Tee
164 schema:givenName Michael
165 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0776772602.31
166 rdf:type schema:Person
167 sg:pub.10.1007/978-3-319-24571-3_18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052617218
168 https://doi.org/10.1007/978-3-319-24571-3_18
169 rdf:type schema:CreativeWork
170 sg:pub.10.1007/978-3-540-35488-8_13 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026061992
171 https://doi.org/10.1007/978-3-540-35488-8_13
172 rdf:type schema:CreativeWork
173 sg:pub.10.1007/bf00994018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025150743
174 https://doi.org/10.1007/bf00994018
175 rdf:type schema:CreativeWork
176 sg:pub.10.1007/s00330-005-0057-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040014494
177 https://doi.org/10.1007/s00330-005-0057-5
178 rdf:type schema:CreativeWork
179 sg:pub.10.1007/s11886-009-0075-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1020440290
180 https://doi.org/10.1007/s11886-009-0075-z
181 rdf:type schema:CreativeWork
182 sg:pub.10.1023/a:1010933404324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024739340
183 https://doi.org/10.1023/a:1010933404324
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1002/mrm.1910380517 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043139910
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1016/j.compbiomed.2015.03.033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028746195
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1016/j.jacc.2012.08.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024243590
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1016/j.media.2005.01.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032778330
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1016/j.media.2012.11.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017989915
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1016/j.rcl.2008.10.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028031824
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1016/s0735-1097(00)01186-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001762979
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1056/nejm200004203421603 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053221725
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1109/isbi.2009.5193259 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093593486
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1109/tmi.2003.815867 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061694447
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1109/tmi.2011.2105274 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061695688
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1109/tmi.2011.2156805 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061695754
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1109/tmi.2011.2171706 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061695817
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1145/1961189.1961199 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013637525
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1148/radiol.2313030132 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007333491
214 rdf:type schema:CreativeWork
215 https://doi.org/10.1161/01.cir.0000089041.69175.9d schema:sameAs https://app.dimensions.ai/details/publication/pub.1050836677
216 rdf:type schema:CreativeWork
217 https://doi.org/10.1161/cir.0000000000000152 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028161404
218 rdf:type schema:CreativeWork
219 https://doi.org/10.1161/circimaging.108.789032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023300241
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1161/circimaging.110.959817 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039112077
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1161/circulationaha.105.521450 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000306762
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1161/hc0402.102975 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039853393
226 rdf:type schema:CreativeWork
227 https://www.grid.ac/institutes/grid.4991.5 schema:alternateName University of Oxford
228 schema:name Institute of Biomedical Engineering, University of Oxford, Oxford, UK
229 Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA
230 rdf:type schema:Organization
 




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


...