High resolution 3D diffusion cardiovascular magnetic resonance of carotid vessel wall to detect lipid core without contrast media View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

Yibin Xie, Wei Yu, Zhaoyang Fan, Christopher Nguyen, Xiaoming Bi, Jing An, Tianjing Zhang, Zhaoqi Zhang, Debiao Li

ABSTRACT

BACKGROUND: Without the need of contrast media, diffusion-weighted imaging (DWI) has shown great promise for accurate detection of lipid-rich necrotic core (LRNC), a well-known feature of vulnerable plaques. However, limited resolution and poor image quality in vivo with conventional single-shot diffusion-weighted echo planar imaging (SS-DWEPI) has hindered its clinical application. The aim of this work is to develop a diffusion-prepared turbo-spin-echo (DP-TSE) technique for carotid plaque characterization with 3D high resolution and improved image quality. METHODS: Unlike SS-DWEPI where the diffusion encoding is integrated in the EPI framework, DP-TSE uses a diffusion encoding module separated from the TSE framework, allowing for segmented acquisition without the sensitivity to phase errors. The interleaved, motion-compensated sequence was designed to enable 3D black-blood DWI of carotid arteries with sub-millimeter resolution. The sequence was tested on 12 healthy subjects and compared with SS-DWEPI for image quality, vessel wall visibility, and vessel wall thickness measurements. A pilot study was performed on 6 patients with carotid plaques using this sequence and compared with conventional contrast-enhanced multi-contrast 2D TSE as the reference. RESULTS: DP-TSE demonstrated advantages over SS-DWEPI for resolution and image quality. In the healthy subjects, vessel wall visibility was significantly higher with diffusion-prepared TSE (p < 0.001). Vessel wall thicknesses measured from diffusion-prepared TSE were on average 35% thinner than those from the EPI images due to less distortion and partial volume effect (p < 0.001). ADC measurements of healthy carotid vessel wall are 1.53 ± 0.23 × 10-3 mm2/s. In patients the mean ADC measurements in the LRNC area were significantly lower (0.60 ± 0.16 × 10-3 mm2/s) than those of the fibrous plaque tissue (1.27 ± 0.29 × 10-3 mm2/s, p < 0.01). CONCLUSIONS: Diffusion-prepared CMR allows, for the first time, 3D DWI of the carotid arterial wall in vivo with high spatial resolution and improved image quality over SS-DWEPI. It can potentially detect LRNC without the use of contrast agents, allowing plaque characterization in patients with renal insufficiency. More... »

PAGES

67

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12968-014-0067-z

DOI

http://dx.doi.org/10.1186/s12968-014-0067-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005604523

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25238168


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/2008/1102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Cardiorespiratory Medicine and Haematology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health 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": "Aged, 80 and over", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Carotid Arteries", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Carotid Artery Diseases", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Case-Control Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Contrast Media", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Diffusion Magnetic Resonance Imaging", 
        "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": "Image Interpretation, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Imaging, Three-Dimensional", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Lipids", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Magnetic Resonance Imaging, Cine", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Necrosis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Pilot Projects", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Plaque, Atherosclerotic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Predictive Value of Tests", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of California Los Angeles", 
          "id": "https://www.grid.ac/institutes/grid.19006.3e", 
          "name": [
            "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA", 
            "Department of Bioengineering, University of California, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xie", 
        "givenName": "Yibin", 
        "id": "sg:person.01320556462.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01320556462.83"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Capital Medical University", 
          "id": "https://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yu", 
        "givenName": "Wei", 
        "id": "sg:person.01143631075.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01143631075.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cedars-Sinai Medical Center", 
          "id": "https://www.grid.ac/institutes/grid.50956.3f", 
          "name": [
            "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fan", 
        "givenName": "Zhaoyang", 
        "id": "sg:person.01136172260.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136172260.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of California Los Angeles", 
          "id": "https://www.grid.ac/institutes/grid.19006.3e", 
          "name": [
            "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA", 
            "Department of Bioengineering, University of California, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nguyen", 
        "givenName": "Christopher", 
        "id": "sg:person.01331047206.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01331047206.43"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "MR R&D, Siemens Healthcare, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bi", 
        "givenName": "Xiaoming", 
        "id": "sg:person.01066244563.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066244563.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "MR Collaborations NE Asia, Siemens Healthcare, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "An", 
        "givenName": "Jing", 
        "id": "sg:person.01340437625.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01340437625.65"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "MR Collaborations NE Asia, Siemens Healthcare, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Tianjing", 
        "id": "sg:person.0620532177.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0620532177.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Capital Medical University", 
          "id": "https://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Zhaoqi", 
        "id": "sg:person.014030237157.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014030237157.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of California Los Angeles", 
          "id": "https://www.grid.ac/institutes/grid.19006.3e", 
          "name": [
            "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA", 
            "Department of Bioengineering, University of California, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Debiao", 
        "id": "sg:person.01152021525.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152021525.33"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/mrm.1910320418", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002745114"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1016/j.nuclcard.2008.02.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003740346", 
          "https://doi.org/10.1016/j.nuclcard.2008.02.001"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/10976640600843413", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007390801"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.22736", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009019545"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.21385", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009186956"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2232010659", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019829323"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.10618", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021851819"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.1880030617", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027269442"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.0000087481.55887.c9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030501597"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.0000087480.94275.97", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030857438"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2005.10.065", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033735973"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.21944", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034846901"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.21944", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034846901"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.1910390613", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036464811"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.1910380404", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037547033"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00234-010-0680-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037879287", 
          "https://doi.org/10.1007/s00234-010-0680-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00234-010-0680-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037879287", 
          "https://doi.org/10.1007/s00234-010-0680-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.22058", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038968064"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.22058", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038968064"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1046/j.1523-1755.2002.00372.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039927423", 
          "https://doi.org/10.1046/j.1523-1755.2002.00372.x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/atvbaha.107.141028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039934942"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/atvbaha.107.141028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039934942"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.104.3.249", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041984558"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1046/j.1523-1755.1999.00422.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044645547", 
          "https://doi.org/10.1046/j.1523-1755.1999.00422.x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.20525", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046641191"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.20525", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046641191"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-2796.1994.tb00847.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047733418"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-2796.1994.tb00847.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047733418"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2441051769", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048921722"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00234-003-1054-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049363157", 
          "https://doi.org/10.1007/s00234-003-1054-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.atv.17.3.542", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063334647"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1227/01.neu.0000239895.00373.e4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064435974"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1077969259", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.156.3.4023236", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1080087192"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-12", 
    "datePublishedReg": "2014-12-01", 
    "description": "BACKGROUND: Without the need of contrast media, diffusion-weighted imaging (DWI) has shown great promise for accurate detection of lipid-rich necrotic core (LRNC), a well-known feature of vulnerable plaques. However, limited resolution and poor image quality in vivo with conventional single-shot diffusion-weighted echo planar imaging (SS-DWEPI) has hindered its clinical application. The aim of this work is to develop a diffusion-prepared turbo-spin-echo (DP-TSE) technique for carotid plaque characterization with 3D high resolution and improved image quality.\nMETHODS: Unlike SS-DWEPI where the diffusion encoding is integrated in the EPI framework, DP-TSE uses a diffusion encoding module separated from the TSE framework, allowing for segmented acquisition without the sensitivity to phase errors. The interleaved, motion-compensated sequence was designed to enable 3D black-blood DWI of carotid arteries with sub-millimeter resolution. The sequence was tested on 12 healthy subjects and compared with SS-DWEPI for image quality, vessel wall visibility, and vessel wall thickness measurements. A pilot study was performed on 6 patients with carotid plaques using this sequence and compared with conventional contrast-enhanced multi-contrast 2D TSE as the reference.\nRESULTS: DP-TSE demonstrated advantages over SS-DWEPI for resolution and image quality. In the healthy subjects, vessel wall visibility was significantly higher with diffusion-prepared TSE (p\u2009<\u20090.001). Vessel wall thicknesses measured from diffusion-prepared TSE were on average 35% thinner than those from the EPI images due to less distortion and partial volume effect (p\u2009<\u20090.001). ADC measurements of healthy carotid vessel wall are 1.53\u2009\u00b1\u20090.23\u2009\u00d7\u200910-3\u00a0mm2/s. In patients the mean ADC measurements in the LRNC area were significantly lower (0.60\u2009\u00b1\u20090.16\u2009\u00d7\u200910-3\u00a0mm2/s) than those of the fibrous plaque tissue (1.27\u2009\u00b1\u20090.29\u2009\u00d7\u200910-3\u00a0mm2/s, p\u2009<\u20090.01).\nCONCLUSIONS: Diffusion-prepared CMR allows, for the first time, 3D DWI of the carotid arterial wall in vivo with high spatial resolution and improved image quality over SS-DWEPI. It can potentially detect LRNC without the use of contrast agents, allowing plaque characterization in patients with renal insufficiency.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s12968-014-0067-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.3801990", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2542522", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1030439", 
        "issn": [
          "1548-7679", 
          "1879-2855"
        ], 
        "name": "Journal of Cardiovascular Magnetic Resonance", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "16"
      }
    ], 
    "name": "High resolution 3D diffusion cardiovascular magnetic resonance of carotid vessel wall to detect lipid core without contrast media", 
    "pagination": "67", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "1ab3eac5f1930aca3d922f6f600a18908901c6522b9c7280263adc7c2c10a5ca"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "25238168"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9815616"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12968-014-0067-z"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1005604523"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12968-014-0067-z", 
      "https://app.dimensions.ai/details/publication/pub.1005604523"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:09", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000367_0000000367/records_88239_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186%2Fs12968-014-0067-z"
  }
]
 

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.10.1186/s12968-014-0067-z'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12968-014-0067-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12968-014-0067-z'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12968-014-0067-z'


 

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

310 TRIPLES      21 PREDICATES      77 URIs      41 LITERALS      29 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12968-014-0067-z schema:about N003e5ecf2c0340ebb21e2a4e7eabfaed
2 N15d9fe565b624922be8631c5381dffa7
3 N23990bfefd6c4211a3ae6cf557b45aac
4 N60b0315ae00b4ce6a094b590687652c3
5 N6235959a462e417aabae6a6835c8b12b
6 N647d8cdffdad453e870c241b2eb6e117
7 N707dc485000346a0bdc3761b0620e276
8 N7b45e93dda8648f5b1c3d0cbe214a810
9 N870d12d392ed4a0abec2d01117037757
10 N9afcea493ec54d25a14740842f54f4c5
11 Nb355347c808b47e392c66d5e8db8d9b4
12 Nb49a454474c441dca4aeec1cd399c2fc
13 Nc4eda72e7ceb4bd89412e36a01aaa5e6
14 Ncb542747e01242ba94dfe84c27e15d26
15 Nd35ac144667e461eb8a12bac9db71915
16 Nd9b86886ec614b679d36631cf7a1bd6d
17 Ndcf3bbe20b414f4aae67560676dbf4d5
18 Ne5833d4eb9fa4c21b23948cf052a42d8
19 Ne76fad84664b422d8f1373a3f7957f40
20 Ne81b7ce658924cdca7a9ac8a6d22f204
21 anzsrc-for:11
22 anzsrc-for:1102
23 schema:author N806521a41a0a4acdad617e68d6b06e0a
24 schema:citation sg:pub.10.1007/s00234-003-1054-5
25 sg:pub.10.1007/s00234-010-0680-y
26 sg:pub.10.1016/j.nuclcard.2008.02.001
27 sg:pub.10.1046/j.1523-1755.1999.00422.x
28 sg:pub.10.1046/j.1523-1755.2002.00372.x
29 https://app.dimensions.ai/details/publication/pub.1077969259
30 https://doi.org/10.1002/jmri.1880030617
31 https://doi.org/10.1002/jmri.20525
32 https://doi.org/10.1002/jmri.21944
33 https://doi.org/10.1002/jmri.22058
34 https://doi.org/10.1002/jmri.22736
35 https://doi.org/10.1002/mrm.10618
36 https://doi.org/10.1002/mrm.1910320418
37 https://doi.org/10.1002/mrm.1910380404
38 https://doi.org/10.1002/mrm.1910390613
39 https://doi.org/10.1002/mrm.21385
40 https://doi.org/10.1016/j.jacc.2005.10.065
41 https://doi.org/10.1080/10976640600843413
42 https://doi.org/10.1111/j.1365-2796.1994.tb00847.x
43 https://doi.org/10.1148/radiol.2232010659
44 https://doi.org/10.1148/radiol.2441051769
45 https://doi.org/10.1148/radiology.156.3.4023236
46 https://doi.org/10.1161/01.atv.17.3.542
47 https://doi.org/10.1161/01.cir.0000087480.94275.97
48 https://doi.org/10.1161/01.cir.0000087481.55887.c9
49 https://doi.org/10.1161/01.cir.104.3.249
50 https://doi.org/10.1161/atvbaha.107.141028
51 https://doi.org/10.1227/01.neu.0000239895.00373.e4
52 schema:datePublished 2014-12
53 schema:datePublishedReg 2014-12-01
54 schema:description BACKGROUND: Without the need of contrast media, diffusion-weighted imaging (DWI) has shown great promise for accurate detection of lipid-rich necrotic core (LRNC), a well-known feature of vulnerable plaques. However, limited resolution and poor image quality in vivo with conventional single-shot diffusion-weighted echo planar imaging (SS-DWEPI) has hindered its clinical application. The aim of this work is to develop a diffusion-prepared turbo-spin-echo (DP-TSE) technique for carotid plaque characterization with 3D high resolution and improved image quality. METHODS: Unlike SS-DWEPI where the diffusion encoding is integrated in the EPI framework, DP-TSE uses a diffusion encoding module separated from the TSE framework, allowing for segmented acquisition without the sensitivity to phase errors. The interleaved, motion-compensated sequence was designed to enable 3D black-blood DWI of carotid arteries with sub-millimeter resolution. The sequence was tested on 12 healthy subjects and compared with SS-DWEPI for image quality, vessel wall visibility, and vessel wall thickness measurements. A pilot study was performed on 6 patients with carotid plaques using this sequence and compared with conventional contrast-enhanced multi-contrast 2D TSE as the reference. RESULTS: DP-TSE demonstrated advantages over SS-DWEPI for resolution and image quality. In the healthy subjects, vessel wall visibility was significantly higher with diffusion-prepared TSE (p < 0.001). Vessel wall thicknesses measured from diffusion-prepared TSE were on average 35% thinner than those from the EPI images due to less distortion and partial volume effect (p < 0.001). ADC measurements of healthy carotid vessel wall are 1.53 ± 0.23 × 10-3 mm2/s. In patients the mean ADC measurements in the LRNC area were significantly lower (0.60 ± 0.16 × 10-3 mm2/s) than those of the fibrous plaque tissue (1.27 ± 0.29 × 10-3 mm2/s, p < 0.01). CONCLUSIONS: Diffusion-prepared CMR allows, for the first time, 3D DWI of the carotid arterial wall in vivo with high spatial resolution and improved image quality over SS-DWEPI. It can potentially detect LRNC without the use of contrast agents, allowing plaque characterization in patients with renal insufficiency.
55 schema:genre research_article
56 schema:inLanguage en
57 schema:isAccessibleForFree true
58 schema:isPartOf N5109dd422eb745bdb72888f7bddd806c
59 Ndecfd483672041a98f9bc638013840df
60 sg:journal.1030439
61 schema:name High resolution 3D diffusion cardiovascular magnetic resonance of carotid vessel wall to detect lipid core without contrast media
62 schema:pagination 67
63 schema:productId N44a382971ad94b248cad947d520396bf
64 N58632371ed7c402a9d8e376ddc3d8fc6
65 N71f864857e0f4bd8a531daa7ab56ded0
66 Na0143fff978549288091fb3284d9abff
67 Nd30295cc695845488d1be0cadf0287c9
68 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005604523
69 https://doi.org/10.1186/s12968-014-0067-z
70 schema:sdDatePublished 2019-04-11T13:09
71 schema:sdLicense https://scigraph.springernature.com/explorer/license/
72 schema:sdPublisher Nbef493715f0d424bba1f0ea1d1bc6b0e
73 schema:url http://link.springer.com/10.1186%2Fs12968-014-0067-z
74 sgo:license sg:explorer/license/
75 sgo:sdDataset articles
76 rdf:type schema:ScholarlyArticle
77 N003e5ecf2c0340ebb21e2a4e7eabfaed schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
78 schema:name Middle Aged
79 rdf:type schema:DefinedTerm
80 N12caccae5d3b4194ad244cf2d63c5e8e rdf:first sg:person.0620532177.31
81 rdf:rest N8a9eafee310e482c8122fb98ef41c32a
82 N15d9fe565b624922be8631c5381dffa7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
83 schema:name Predictive Value of Tests
84 rdf:type schema:DefinedTerm
85 N1816de96214f4b429045f751427d9ffd schema:name MR Collaborations NE Asia, Siemens Healthcare, Beijing, China
86 rdf:type schema:Organization
87 N23990bfefd6c4211a3ae6cf557b45aac schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
88 schema:name Imaging, Three-Dimensional
89 rdf:type schema:DefinedTerm
90 N418d346edefc428387b9aa1af4cbf28c rdf:first sg:person.01066244563.44
91 rdf:rest Nf59e666d6ca243b4a9a3cc6c03d8f96f
92 N44a382971ad94b248cad947d520396bf schema:name readcube_id
93 schema:value 1ab3eac5f1930aca3d922f6f600a18908901c6522b9c7280263adc7c2c10a5ca
94 rdf:type schema:PropertyValue
95 N5109dd422eb745bdb72888f7bddd806c schema:volumeNumber 16
96 rdf:type schema:PublicationVolume
97 N583aa59843b5410a9335c692066e9188 rdf:first sg:person.01331047206.43
98 rdf:rest N418d346edefc428387b9aa1af4cbf28c
99 N58632371ed7c402a9d8e376ddc3d8fc6 schema:name nlm_unique_id
100 schema:value 9815616
101 rdf:type schema:PropertyValue
102 N60b0315ae00b4ce6a094b590687652c3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
103 schema:name Image Interpretation, Computer-Assisted
104 rdf:type schema:DefinedTerm
105 N6235959a462e417aabae6a6835c8b12b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
106 schema:name Aged, 80 and over
107 rdf:type schema:DefinedTerm
108 N647d8cdffdad453e870c241b2eb6e117 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Male
110 rdf:type schema:DefinedTerm
111 N707dc485000346a0bdc3761b0620e276 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Magnetic Resonance Imaging, Cine
113 rdf:type schema:DefinedTerm
114 N71f864857e0f4bd8a531daa7ab56ded0 schema:name doi
115 schema:value 10.1186/s12968-014-0067-z
116 rdf:type schema:PropertyValue
117 N789aca6d8b9f4f74891db0422df9b829 rdf:first sg:person.01143631075.45
118 rdf:rest Nbd22e91c3e0e4feca77512d63f741877
119 N7b45e93dda8648f5b1c3d0cbe214a810 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Contrast Media
121 rdf:type schema:DefinedTerm
122 N806521a41a0a4acdad617e68d6b06e0a rdf:first sg:person.01320556462.83
123 rdf:rest N789aca6d8b9f4f74891db0422df9b829
124 N870d12d392ed4a0abec2d01117037757 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Adult
126 rdf:type schema:DefinedTerm
127 N8a9eafee310e482c8122fb98ef41c32a rdf:first sg:person.014030237157.28
128 rdf:rest Nbf27c8080aca4b7a8fe21a933a3c6022
129 N8b7f713db3d04a35b18f2757404e5996 schema:name MR Collaborations NE Asia, Siemens Healthcare, Beijing, China
130 rdf:type schema:Organization
131 N907f6de720844617b0d3f93427235a38 schema:name MR R&D, Siemens Healthcare, Los Angeles, CA, USA
132 rdf:type schema:Organization
133 N9afcea493ec54d25a14740842f54f4c5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Lipids
135 rdf:type schema:DefinedTerm
136 Na0143fff978549288091fb3284d9abff schema:name pubmed_id
137 schema:value 25238168
138 rdf:type schema:PropertyValue
139 Nb355347c808b47e392c66d5e8db8d9b4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
140 schema:name Humans
141 rdf:type schema:DefinedTerm
142 Nb49a454474c441dca4aeec1cd399c2fc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Case-Control Studies
144 rdf:type schema:DefinedTerm
145 Nbd22e91c3e0e4feca77512d63f741877 rdf:first sg:person.01136172260.58
146 rdf:rest N583aa59843b5410a9335c692066e9188
147 Nbef493715f0d424bba1f0ea1d1bc6b0e schema:name Springer Nature - SN SciGraph project
148 rdf:type schema:Organization
149 Nbf27c8080aca4b7a8fe21a933a3c6022 rdf:first sg:person.01152021525.33
150 rdf:rest rdf:nil
151 Nc4eda72e7ceb4bd89412e36a01aaa5e6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
152 schema:name Necrosis
153 rdf:type schema:DefinedTerm
154 Ncb542747e01242ba94dfe84c27e15d26 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
155 schema:name Female
156 rdf:type schema:DefinedTerm
157 Nd30295cc695845488d1be0cadf0287c9 schema:name dimensions_id
158 schema:value pub.1005604523
159 rdf:type schema:PropertyValue
160 Nd35ac144667e461eb8a12bac9db71915 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
161 schema:name Plaque, Atherosclerotic
162 rdf:type schema:DefinedTerm
163 Nd9b86886ec614b679d36631cf7a1bd6d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
164 schema:name Carotid Arteries
165 rdf:type schema:DefinedTerm
166 Ndcf3bbe20b414f4aae67560676dbf4d5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
167 schema:name Diffusion Magnetic Resonance Imaging
168 rdf:type schema:DefinedTerm
169 Ndecfd483672041a98f9bc638013840df schema:issueNumber 1
170 rdf:type schema:PublicationIssue
171 Ne5833d4eb9fa4c21b23948cf052a42d8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
172 schema:name Carotid Artery Diseases
173 rdf:type schema:DefinedTerm
174 Ne76fad84664b422d8f1373a3f7957f40 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
175 schema:name Aged
176 rdf:type schema:DefinedTerm
177 Ne81b7ce658924cdca7a9ac8a6d22f204 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
178 schema:name Pilot Projects
179 rdf:type schema:DefinedTerm
180 Nf59e666d6ca243b4a9a3cc6c03d8f96f rdf:first sg:person.01340437625.65
181 rdf:rest N12caccae5d3b4194ad244cf2d63c5e8e
182 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
183 schema:name Medical and Health Sciences
184 rdf:type schema:DefinedTerm
185 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
186 schema:name Cardiorespiratory Medicine and Haematology
187 rdf:type schema:DefinedTerm
188 sg:grant.2542522 http://pending.schema.org/fundedItem sg:pub.10.1186/s12968-014-0067-z
189 rdf:type schema:MonetaryGrant
190 sg:grant.3801990 http://pending.schema.org/fundedItem sg:pub.10.1186/s12968-014-0067-z
191 rdf:type schema:MonetaryGrant
192 sg:journal.1030439 schema:issn 1548-7679
193 1879-2855
194 schema:name Journal of Cardiovascular Magnetic Resonance
195 rdf:type schema:Periodical
196 sg:person.01066244563.44 schema:affiliation N907f6de720844617b0d3f93427235a38
197 schema:familyName Bi
198 schema:givenName Xiaoming
199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066244563.44
200 rdf:type schema:Person
201 sg:person.01136172260.58 schema:affiliation https://www.grid.ac/institutes/grid.50956.3f
202 schema:familyName Fan
203 schema:givenName Zhaoyang
204 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136172260.58
205 rdf:type schema:Person
206 sg:person.01143631075.45 schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
207 schema:familyName Yu
208 schema:givenName Wei
209 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01143631075.45
210 rdf:type schema:Person
211 sg:person.01152021525.33 schema:affiliation https://www.grid.ac/institutes/grid.19006.3e
212 schema:familyName Li
213 schema:givenName Debiao
214 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152021525.33
215 rdf:type schema:Person
216 sg:person.01320556462.83 schema:affiliation https://www.grid.ac/institutes/grid.19006.3e
217 schema:familyName Xie
218 schema:givenName Yibin
219 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01320556462.83
220 rdf:type schema:Person
221 sg:person.01331047206.43 schema:affiliation https://www.grid.ac/institutes/grid.19006.3e
222 schema:familyName Nguyen
223 schema:givenName Christopher
224 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01331047206.43
225 rdf:type schema:Person
226 sg:person.01340437625.65 schema:affiliation N1816de96214f4b429045f751427d9ffd
227 schema:familyName An
228 schema:givenName Jing
229 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01340437625.65
230 rdf:type schema:Person
231 sg:person.014030237157.28 schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
232 schema:familyName Zhang
233 schema:givenName Zhaoqi
234 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014030237157.28
235 rdf:type schema:Person
236 sg:person.0620532177.31 schema:affiliation N8b7f713db3d04a35b18f2757404e5996
237 schema:familyName Zhang
238 schema:givenName Tianjing
239 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0620532177.31
240 rdf:type schema:Person
241 sg:pub.10.1007/s00234-003-1054-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049363157
242 https://doi.org/10.1007/s00234-003-1054-5
243 rdf:type schema:CreativeWork
244 sg:pub.10.1007/s00234-010-0680-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1037879287
245 https://doi.org/10.1007/s00234-010-0680-y
246 rdf:type schema:CreativeWork
247 sg:pub.10.1016/j.nuclcard.2008.02.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003740346
248 https://doi.org/10.1016/j.nuclcard.2008.02.001
249 rdf:type schema:CreativeWork
250 sg:pub.10.1046/j.1523-1755.1999.00422.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1044645547
251 https://doi.org/10.1046/j.1523-1755.1999.00422.x
252 rdf:type schema:CreativeWork
253 sg:pub.10.1046/j.1523-1755.2002.00372.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1039927423
254 https://doi.org/10.1046/j.1523-1755.2002.00372.x
255 rdf:type schema:CreativeWork
256 https://app.dimensions.ai/details/publication/pub.1077969259 schema:CreativeWork
257 https://doi.org/10.1002/jmri.1880030617 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027269442
258 rdf:type schema:CreativeWork
259 https://doi.org/10.1002/jmri.20525 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046641191
260 rdf:type schema:CreativeWork
261 https://doi.org/10.1002/jmri.21944 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034846901
262 rdf:type schema:CreativeWork
263 https://doi.org/10.1002/jmri.22058 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038968064
264 rdf:type schema:CreativeWork
265 https://doi.org/10.1002/jmri.22736 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009019545
266 rdf:type schema:CreativeWork
267 https://doi.org/10.1002/mrm.10618 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021851819
268 rdf:type schema:CreativeWork
269 https://doi.org/10.1002/mrm.1910320418 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002745114
270 rdf:type schema:CreativeWork
271 https://doi.org/10.1002/mrm.1910380404 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037547033
272 rdf:type schema:CreativeWork
273 https://doi.org/10.1002/mrm.1910390613 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036464811
274 rdf:type schema:CreativeWork
275 https://doi.org/10.1002/mrm.21385 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009186956
276 rdf:type schema:CreativeWork
277 https://doi.org/10.1016/j.jacc.2005.10.065 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033735973
278 rdf:type schema:CreativeWork
279 https://doi.org/10.1080/10976640600843413 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007390801
280 rdf:type schema:CreativeWork
281 https://doi.org/10.1111/j.1365-2796.1994.tb00847.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1047733418
282 rdf:type schema:CreativeWork
283 https://doi.org/10.1148/radiol.2232010659 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019829323
284 rdf:type schema:CreativeWork
285 https://doi.org/10.1148/radiol.2441051769 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048921722
286 rdf:type schema:CreativeWork
287 https://doi.org/10.1148/radiology.156.3.4023236 schema:sameAs https://app.dimensions.ai/details/publication/pub.1080087192
288 rdf:type schema:CreativeWork
289 https://doi.org/10.1161/01.atv.17.3.542 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063334647
290 rdf:type schema:CreativeWork
291 https://doi.org/10.1161/01.cir.0000087480.94275.97 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030857438
292 rdf:type schema:CreativeWork
293 https://doi.org/10.1161/01.cir.0000087481.55887.c9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030501597
294 rdf:type schema:CreativeWork
295 https://doi.org/10.1161/01.cir.104.3.249 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041984558
296 rdf:type schema:CreativeWork
297 https://doi.org/10.1161/atvbaha.107.141028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039934942
298 rdf:type schema:CreativeWork
299 https://doi.org/10.1227/01.neu.0000239895.00373.e4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064435974
300 rdf:type schema:CreativeWork
301 https://www.grid.ac/institutes/grid.19006.3e schema:alternateName University of California Los Angeles
302 schema:name Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
303 Department of Bioengineering, University of California, Los Angeles, CA, USA
304 rdf:type schema:Organization
305 https://www.grid.ac/institutes/grid.24696.3f schema:alternateName Capital Medical University
306 schema:name Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China
307 rdf:type schema:Organization
308 https://www.grid.ac/institutes/grid.50956.3f schema:alternateName Cedars-Sinai Medical Center
309 schema:name Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
310 rdf:type schema:Organization
 




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


...