Black-blood thrombus imaging (BTI): a contrast-free cardiovascular magnetic resonance approach for the diagnosis of non-acute deep vein thrombosis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-01-18

AUTHORS

Guoxi Xie, Hanwei Chen, Xueping He, Jianke Liang, Wei Deng, Zhuonan He, Yufeng Ye, Qi Yang, Xiaoming Bi, Xin Liu, Debiao Li, Zhaoyang Fan

ABSTRACT

BackgroundDeep vein thrombosis (DVT) is a common but elusive illness that can result in long-term disability or death. Accurate detection of thrombosis and assessment of its size and distribution are critical for treatment decision-making. In the present study, we sought to develop and evaluate a cardiovascular magnetic resonance (CMR) black-blood thrombus imaging (BTI) technique, based on delay alternating with nutation for tailored excitation black-blood preparation and variable flip angle turbo-spin-echo readout, for the diagnosis of non-acute DVT.MethodsThis prospective study was approved by institutional review board and informed consent obtained from all subjects. BTI was first conducted in 11 healthy subjects for parameter optimization and then conducted in 18 non-acute DVT patients to evaluate its diagnostic performance. Two clinically used CMR techniques, contrast-enhanced CMR venography (CE-MRV) and three dimensional magnetization prepared rapid acquisition gradient echo (MPRAGE), were also conducted in all patients for comparison. All images obtained from patients were analyzed on a per-segment basis. Using the consensus diagnosis of CE-MRV as the reference, the sensitivity (SE), specificity (SP), positive and negative predictive values (PPV and NPV), and accuracy (ACC) of BTI and MPRAGE as well as their diagnostic agreement with CE-MRV were calculated. Besides, diagnostic confidence and interreader diagnostic agreement were evaluated for all three techniques.ResultsBTI with optimized parameters effectively nulled the venous blood flow signal and allowed directly visualizing the thrombus within the black-blood lumen. Higher SE (90.4% vs 67.6%), SP (99.0% vs. 97.4%), PPV (95.4% vs. 85.6%), NPV (97.8% vs 92.9%) and ACC (97.4% vs. 91.8%) were obtained by BTI in comparison with MPRAGE. Good diagnostic confidence and excellent diagnostic and interreader agreements were achieved by BTI, which were superior to MPRAGE on detecting the chronic thrombus.ConclusionBTI allows direct visualization of non-acute DVT within the dark venous lumen and has the potential to be a reliable diagnostic tool without the use of contrast medium. More... »

PAGES

4

References to SciGraph publications

  • 2005-10-03. Systematic review and meta-analysis of the diagnostic accuracy of ultrasonography for deep vein thrombosis in BMC MEDICAL IMAGING
  • 2009-11-28. 3D T1-mapping for the characterization of deep vein thrombosis in MAGNETIC RESONANCE MATERIALS IN PHYSICS, BIOLOGY AND MEDICINE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12968-016-0320-8

    DOI

    http://dx.doi.org/10.1186/s12968-016-0320-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1014715046

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28095878


    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/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "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"
          }, 
          {
            "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": "Case-Control Studies", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Chronic Disease", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Contrast Media", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Female", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gadolinium DTPA", 
            "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": "Magnetic Resonance Angiography", 
            "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": "Observer Variation", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Phlebography", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Predictive Value of Tests", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Prospective Studies", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Reproducibility of Results", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Venous Thrombosis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Young Adult", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Pacific Theatres Building, Suite 800, 8700 Beverly Blvd, 90048, Los Angeles, CA, USA", 
              "id": "http://www.grid.ac/institutes/grid.50956.3f", 
              "name": [
                "Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, CAS, 518055, Guangdong, China", 
                "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Pacific Theatres Building, Suite 800, 8700 Beverly Blvd, 90048, Los Angeles, CA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Xie", 
            "givenName": "Guoxi", 
            "id": "sg:person.01240364572.83", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240364572.83"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China", 
              "id": "http://www.grid.ac/institutes/grid.459864.2", 
              "name": [
                "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Chen", 
            "givenName": "Hanwei", 
            "id": "sg:person.0607062163.73", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0607062163.73"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Guangzhou University of Chinese Medicine, 510405, Guangzhou, Guangdong, China", 
              "id": "http://www.grid.ac/institutes/grid.411866.c", 
              "name": [
                "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China", 
                "Guangzhou University of Chinese Medicine, 510405, Guangzhou, Guangdong, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "He", 
            "givenName": "Xueping", 
            "id": "sg:person.016055225063.44", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016055225063.44"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China", 
              "id": "http://www.grid.ac/institutes/grid.459864.2", 
              "name": [
                "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Liang", 
            "givenName": "Jianke", 
            "id": "sg:person.016652605463.52", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016652605463.52"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China", 
              "id": "http://www.grid.ac/institutes/grid.459864.2", 
              "name": [
                "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Deng", 
            "givenName": "Wei", 
            "id": "sg:person.07356014763.21", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07356014763.21"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China", 
              "id": "http://www.grid.ac/institutes/grid.459864.2", 
              "name": [
                "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "He", 
            "givenName": "Zhuonan", 
            "id": "sg:person.010153375363.55", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010153375363.55"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China", 
              "id": "http://www.grid.ac/institutes/grid.459864.2", 
              "name": [
                "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ye", 
            "givenName": "Yufeng", 
            "id": "sg:person.010750755763.16", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010750755763.16"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Radiology, Xuanwu Hospital, 100053, Beijing, China", 
              "id": "http://www.grid.ac/institutes/grid.413259.8", 
              "name": [
                "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Pacific Theatres Building, Suite 800, 8700 Beverly Blvd, 90048, Los Angeles, CA, USA", 
                "Department of Radiology, Xuanwu Hospital, 100053, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Yang", 
            "givenName": "Qi", 
            "id": "sg:person.01353064311.62", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01353064311.62"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "MR R&D, Siemens Healthcare, 90048, Los Angeles, CA, USA", 
              "id": "http://www.grid.ac/institutes/grid.415886.6", 
              "name": [
                "MR R&D, Siemens Healthcare, 90048, 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": {
              "alternateName": "Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, CAS, 518055, Guangdong, China", 
              "id": "http://www.grid.ac/institutes/grid.458489.c", 
              "name": [
                "Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, CAS, 518055, Guangdong, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Liu", 
            "givenName": "Xin", 
            "id": "sg:person.015547527234.48", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015547527234.48"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Pacific Theatres Building, Suite 800, 8700 Beverly Blvd, 90048, Los Angeles, CA, USA", 
              "id": "http://www.grid.ac/institutes/grid.50956.3f", 
              "name": [
                "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Pacific Theatres Building, Suite 800, 8700 Beverly Blvd, 90048, 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"
          }, 
          {
            "affiliation": {
              "alternateName": "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Pacific Theatres Building, Suite 800, 8700 Beverly Blvd, 90048, Los Angeles, CA, USA", 
              "id": "http://www.grid.ac/institutes/grid.50956.3f", 
              "name": [
                "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Pacific Theatres Building, Suite 800, 8700 Beverly Blvd, 90048, 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"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1186/1471-2342-5-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048837898", 
              "https://doi.org/10.1186/1471-2342-5-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10334-009-0189-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042071819", 
              "https://doi.org/10.1007/s10334-009-0189-8"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-01-18", 
        "datePublishedReg": "2017-01-18", 
        "description": "BackgroundDeep vein thrombosis (DVT) is a common but elusive illness that can result in long-term disability or death. Accurate detection of thrombosis and assessment of its size and distribution are critical for treatment decision-making. In the present study, we sought to develop and evaluate a cardiovascular magnetic resonance (CMR) black-blood thrombus imaging (BTI) technique, based on delay alternating with nutation for tailored excitation black-blood preparation and variable flip angle turbo-spin-echo readout, for the diagnosis of non-acute DVT.MethodsThis prospective study was approved by institutional review board and informed consent obtained from all subjects. BTI was first conducted in 11 healthy subjects for parameter optimization and then conducted in 18 non-acute DVT patients to evaluate its diagnostic performance. Two clinically used CMR techniques, contrast-enhanced CMR venography (CE-MRV) and three dimensional magnetization prepared rapid acquisition gradient echo (MPRAGE), were also conducted in all patients for comparison. All images obtained from patients were analyzed on a per-segment basis. Using the consensus diagnosis of CE-MRV as the reference, the sensitivity (SE), specificity (SP), positive and negative predictive values (PPV and NPV), and accuracy (ACC) of BTI and MPRAGE as well as their diagnostic agreement with CE-MRV were calculated. Besides, diagnostic confidence and interreader diagnostic agreement were evaluated for all three techniques.ResultsBTI with optimized parameters effectively nulled the venous blood flow signal and allowed directly visualizing the thrombus within the black-blood lumen. Higher SE (90.4% vs 67.6%), SP (99.0% vs. 97.4%), PPV (95.4% vs. 85.6%), NPV (97.8% vs 92.9%) and ACC (97.4% vs. 91.8%) were obtained by BTI in comparison with MPRAGE. Good diagnostic confidence and excellent diagnostic and interreader agreements were achieved by BTI, which were superior to MPRAGE on detecting the chronic thrombus.ConclusionBTI allows direct visualization of non-acute DVT within the dark venous lumen and has the potential to be a reliable diagnostic tool without the use of contrast medium.", 
        "genre": "article", 
        "id": "sg:pub.10.1186/s12968-016-0320-8", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.8350505", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.100067747", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.7195618", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1030439", 
            "issn": [
              "1548-7679", 
              "1879-2855"
            ], 
            "name": "Journal of Cardiovascular Magnetic Resonance", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "19"
          }
        ], 
        "keywords": [
          "vein thrombosis", 
          "CE-MRV", 
          "diagnostic agreement", 
          "diagnostic confidence", 
          "BackgroundDeep vein thrombosis", 
          "deep vein thrombosis", 
          "long-term disability", 
          "MethodsThis prospective study", 
          "negative predictive value", 
          "institutional review board", 
          "blood flow signals", 
          "chronic thrombus", 
          "reliable diagnostic tool", 
          "better diagnostic confidence", 
          "prospective study", 
          "DVT patients", 
          "rapid acquisition gradient echo", 
          "venous lumen", 
          "healthy subjects", 
          "consensus diagnosis", 
          "predictive value", 
          "contrast medium", 
          "thrombosis", 
          "informed consent", 
          "thrombus imaging", 
          "review board", 
          "patients", 
          "CMR techniques", 
          "diagnostic performance", 
          "black-blood preparation", 
          "interreader agreement", 
          "diagnosis", 
          "DVT", 
          "thrombus", 
          "diagnostic tool", 
          "flow signals", 
          "segment basis", 
          "present study", 
          "MPRAGE", 
          "direct visualization", 
          "imaging techniques", 
          "lumen", 
          "gradient echo", 
          "subjects", 
          "specificity", 
          "venography", 
          "illness", 
          "death", 
          "disability", 
          "sensitivity", 
          "treatment", 
          "consent", 
          "variable flip angle", 
          "study", 
          "PPV", 
          "magnetic resonance approach", 
          "imaging", 
          "NPV", 
          "high sensitivity", 
          "accurate detection", 
          "assessment", 
          "flip angle", 
          "confidence", 
          "echo readout", 
          "comparison", 
          "use", 
          "technique", 
          "detection", 
          "delay", 
          "potential", 
          "preparation", 
          "visualization", 
          "resonance approach", 
          "tool", 
          "echoes", 
          "values", 
          "basis", 
          "accuracy", 
          "reference", 
          "size", 
          "medium", 
          "board", 
          "approach", 
          "parameters", 
          "readout", 
          "signals", 
          "images", 
          "agreement", 
          "distribution", 
          "angle", 
          "performance", 
          "optimization", 
          "optimized parameters", 
          "nutation", 
          "parameter optimization", 
          "magnetization"
        ], 
        "name": "Black-blood thrombus imaging (BTI): a contrast-free cardiovascular magnetic resonance approach for the diagnosis of non-acute deep vein thrombosis", 
        "pagination": "4", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1014715046"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s12968-016-0320-8"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "28095878"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s12968-016-0320-8", 
          "https://app.dimensions.ai/details/publication/pub.1014715046"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:37", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_740.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1186/s12968-016-0320-8"
      }
    ]
     

    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/s12968-016-0320-8'

    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/s12968-016-0320-8'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12968-016-0320-8'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12968-016-0320-8'


     

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

    346 TRIPLES      21 PREDICATES      143 URIs      133 LITERALS      27 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s12968-016-0320-8 schema:about N06750889c5ff4a5b94b216943558ec14
    2 N244188ee74194798bd0659589cbf431e
    3 N2492eae35c2c46aa947bfb36c7ccc297
    4 N31a2c344217d415a81d0762c672012fb
    5 N3624f9b6d883414a9960fb4a5c4d9eed
    6 N5272c0af5bb849909cfce4743704558f
    7 N6716039960f34a538112473c50192304
    8 N6f2b543fad4a40d6beb1d9bc3c46ee3a
    9 N76392d721fa24d228f883916171b67a3
    10 N924a50a237874bf4932700cb2f0ab0e4
    11 N9e8c240a52314f68995bef6fde4816bc
    12 Na8aa7150f44b42148b31baa34467a147
    13 Nb0f8636dd33540f8ac123c50fefc625f
    14 Nb2f54b21e7f5463d942b240d3881f3f9
    15 Nc46bd91e2ac94089a25d63e10bb0f7aa
    16 Nd15a9d14f3e541dbbf7047c83de5c150
    17 Nd1a2a5edb08c4eec95432e76a5fd3be6
    18 Ne79ae0e603684bcfbb8da7ff598e5ac1
    19 Neea84463d01a4925a83806c13639bc90
    20 Nf20d33ea353d4021891715d630eb14c4
    21 anzsrc-for:11
    22 anzsrc-for:1102
    23 schema:author N9a66709b057e421c8fd7a71ef7ef85b2
    24 schema:citation sg:pub.10.1007/s10334-009-0189-8
    25 sg:pub.10.1186/1471-2342-5-6
    26 schema:datePublished 2017-01-18
    27 schema:datePublishedReg 2017-01-18
    28 schema:description BackgroundDeep vein thrombosis (DVT) is a common but elusive illness that can result in long-term disability or death. Accurate detection of thrombosis and assessment of its size and distribution are critical for treatment decision-making. In the present study, we sought to develop and evaluate a cardiovascular magnetic resonance (CMR) black-blood thrombus imaging (BTI) technique, based on delay alternating with nutation for tailored excitation black-blood preparation and variable flip angle turbo-spin-echo readout, for the diagnosis of non-acute DVT.MethodsThis prospective study was approved by institutional review board and informed consent obtained from all subjects. BTI was first conducted in 11 healthy subjects for parameter optimization and then conducted in 18 non-acute DVT patients to evaluate its diagnostic performance. Two clinically used CMR techniques, contrast-enhanced CMR venography (CE-MRV) and three dimensional magnetization prepared rapid acquisition gradient echo (MPRAGE), were also conducted in all patients for comparison. All images obtained from patients were analyzed on a per-segment basis. Using the consensus diagnosis of CE-MRV as the reference, the sensitivity (SE), specificity (SP), positive and negative predictive values (PPV and NPV), and accuracy (ACC) of BTI and MPRAGE as well as their diagnostic agreement with CE-MRV were calculated. Besides, diagnostic confidence and interreader diagnostic agreement were evaluated for all three techniques.ResultsBTI with optimized parameters effectively nulled the venous blood flow signal and allowed directly visualizing the thrombus within the black-blood lumen. Higher SE (90.4% vs 67.6%), SP (99.0% vs. 97.4%), PPV (95.4% vs. 85.6%), NPV (97.8% vs 92.9%) and ACC (97.4% vs. 91.8%) were obtained by BTI in comparison with MPRAGE. Good diagnostic confidence and excellent diagnostic and interreader agreements were achieved by BTI, which were superior to MPRAGE on detecting the chronic thrombus.ConclusionBTI allows direct visualization of non-acute DVT within the dark venous lumen and has the potential to be a reliable diagnostic tool without the use of contrast medium.
    29 schema:genre article
    30 schema:isAccessibleForFree true
    31 schema:isPartOf N0fe2d4caaef44744962a9e239d3c71d8
    32 N35e4d8610f954044bae0d34743b1b618
    33 sg:journal.1030439
    34 schema:keywords BackgroundDeep vein thrombosis
    35 CE-MRV
    36 CMR techniques
    37 DVT
    38 DVT patients
    39 MPRAGE
    40 MethodsThis prospective study
    41 NPV
    42 PPV
    43 accuracy
    44 accurate detection
    45 agreement
    46 angle
    47 approach
    48 assessment
    49 basis
    50 better diagnostic confidence
    51 black-blood preparation
    52 blood flow signals
    53 board
    54 chronic thrombus
    55 comparison
    56 confidence
    57 consensus diagnosis
    58 consent
    59 contrast medium
    60 death
    61 deep vein thrombosis
    62 delay
    63 detection
    64 diagnosis
    65 diagnostic agreement
    66 diagnostic confidence
    67 diagnostic performance
    68 diagnostic tool
    69 direct visualization
    70 disability
    71 distribution
    72 echo readout
    73 echoes
    74 flip angle
    75 flow signals
    76 gradient echo
    77 healthy subjects
    78 high sensitivity
    79 illness
    80 images
    81 imaging
    82 imaging techniques
    83 informed consent
    84 institutional review board
    85 interreader agreement
    86 long-term disability
    87 lumen
    88 magnetic resonance approach
    89 magnetization
    90 medium
    91 negative predictive value
    92 nutation
    93 optimization
    94 optimized parameters
    95 parameter optimization
    96 parameters
    97 patients
    98 performance
    99 potential
    100 predictive value
    101 preparation
    102 present study
    103 prospective study
    104 rapid acquisition gradient echo
    105 readout
    106 reference
    107 reliable diagnostic tool
    108 resonance approach
    109 review board
    110 segment basis
    111 sensitivity
    112 signals
    113 size
    114 specificity
    115 study
    116 subjects
    117 technique
    118 thrombosis
    119 thrombus
    120 thrombus imaging
    121 tool
    122 treatment
    123 use
    124 values
    125 variable flip angle
    126 vein thrombosis
    127 venography
    128 venous lumen
    129 visualization
    130 schema:name Black-blood thrombus imaging (BTI): a contrast-free cardiovascular magnetic resonance approach for the diagnosis of non-acute deep vein thrombosis
    131 schema:pagination 4
    132 schema:productId N1c980ee024f342438b33e53d4726207f
    133 Na70b13095171489d93437c548a5b30d0
    134 Nd3f97bf844bc473d80118b26d1a86133
    135 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014715046
    136 https://doi.org/10.1186/s12968-016-0320-8
    137 schema:sdDatePublished 2022-12-01T06:37
    138 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    139 schema:sdPublisher N82717070950048c2a9042fa705b0b5c4
    140 schema:url https://doi.org/10.1186/s12968-016-0320-8
    141 sgo:license sg:explorer/license/
    142 sgo:sdDataset articles
    143 rdf:type schema:ScholarlyArticle
    144 N06750889c5ff4a5b94b216943558ec14 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    145 schema:name Image Interpretation, Computer-Assisted
    146 rdf:type schema:DefinedTerm
    147 N078161cb46a449098aba84dffbb18bcf rdf:first sg:person.07356014763.21
    148 rdf:rest N1bb2bdf2988e485699b9ce20aa68cda6
    149 N0fe2d4caaef44744962a9e239d3c71d8 schema:volumeNumber 19
    150 rdf:type schema:PublicationVolume
    151 N150e980413fe4b16a8255a977a8b6fae rdf:first sg:person.016652605463.52
    152 rdf:rest N078161cb46a449098aba84dffbb18bcf
    153 N1785a06a42f14e40bcf9cecf2606ed8e rdf:first sg:person.01152021525.33
    154 rdf:rest Nbd3222e7786b44a9a789c50a5edfca62
    155 N1bb2bdf2988e485699b9ce20aa68cda6 rdf:first sg:person.010153375363.55
    156 rdf:rest N1cc92cd03ff74ad6be5fe5273cd94252
    157 N1c980ee024f342438b33e53d4726207f schema:name pubmed_id
    158 schema:value 28095878
    159 rdf:type schema:PropertyValue
    160 N1cc92cd03ff74ad6be5fe5273cd94252 rdf:first sg:person.010750755763.16
    161 rdf:rest N585af78f2da7435eaeedb6726781f73f
    162 N244188ee74194798bd0659589cbf431e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    163 schema:name Reproducibility of Results
    164 rdf:type schema:DefinedTerm
    165 N2492eae35c2c46aa947bfb36c7ccc297 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    166 schema:name Aged
    167 rdf:type schema:DefinedTerm
    168 N31a2c344217d415a81d0762c672012fb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    169 schema:name Male
    170 rdf:type schema:DefinedTerm
    171 N32d5d7d7c0774a82863ef98ad076b443 rdf:first sg:person.0607062163.73
    172 rdf:rest Na73276457d1b4697b0263e8e9330741a
    173 N35e4d8610f954044bae0d34743b1b618 schema:issueNumber 1
    174 rdf:type schema:PublicationIssue
    175 N3624f9b6d883414a9960fb4a5c4d9eed schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    176 schema:name Imaging, Three-Dimensional
    177 rdf:type schema:DefinedTerm
    178 N5272c0af5bb849909cfce4743704558f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    179 schema:name Adult
    180 rdf:type schema:DefinedTerm
    181 N585af78f2da7435eaeedb6726781f73f rdf:first sg:person.01353064311.62
    182 rdf:rest Nceef499ec2d542a4b2d68b2c3c773548
    183 N6716039960f34a538112473c50192304 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    184 schema:name Chronic Disease
    185 rdf:type schema:DefinedTerm
    186 N6f2b543fad4a40d6beb1d9bc3c46ee3a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    187 schema:name Observer Variation
    188 rdf:type schema:DefinedTerm
    189 N76392d721fa24d228f883916171b67a3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    190 schema:name Venous Thrombosis
    191 rdf:type schema:DefinedTerm
    192 N82717070950048c2a9042fa705b0b5c4 schema:name Springer Nature - SN SciGraph project
    193 rdf:type schema:Organization
    194 N924a50a237874bf4932700cb2f0ab0e4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    195 schema:name Case-Control Studies
    196 rdf:type schema:DefinedTerm
    197 N9a66709b057e421c8fd7a71ef7ef85b2 rdf:first sg:person.01240364572.83
    198 rdf:rest N32d5d7d7c0774a82863ef98ad076b443
    199 N9e8c240a52314f68995bef6fde4816bc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    200 schema:name Middle Aged
    201 rdf:type schema:DefinedTerm
    202 Na70b13095171489d93437c548a5b30d0 schema:name dimensions_id
    203 schema:value pub.1014715046
    204 rdf:type schema:PropertyValue
    205 Na73276457d1b4697b0263e8e9330741a rdf:first sg:person.016055225063.44
    206 rdf:rest N150e980413fe4b16a8255a977a8b6fae
    207 Na8aa7150f44b42148b31baa34467a147 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    208 schema:name Phlebography
    209 rdf:type schema:DefinedTerm
    210 Nb0f8636dd33540f8ac123c50fefc625f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    211 schema:name Contrast Media
    212 rdf:type schema:DefinedTerm
    213 Nb2f54b21e7f5463d942b240d3881f3f9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    214 schema:name Humans
    215 rdf:type schema:DefinedTerm
    216 Nbd3222e7786b44a9a789c50a5edfca62 rdf:first sg:person.01136172260.58
    217 rdf:rest rdf:nil
    218 Nc46bd91e2ac94089a25d63e10bb0f7aa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    219 schema:name Female
    220 rdf:type schema:DefinedTerm
    221 Nceef499ec2d542a4b2d68b2c3c773548 rdf:first sg:person.01066244563.44
    222 rdf:rest Ndb0abe5280514ec58a8afd729384e74b
    223 Nd15a9d14f3e541dbbf7047c83de5c150 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    224 schema:name Predictive Value of Tests
    225 rdf:type schema:DefinedTerm
    226 Nd1a2a5edb08c4eec95432e76a5fd3be6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    227 schema:name Magnetic Resonance Angiography
    228 rdf:type schema:DefinedTerm
    229 Nd3f97bf844bc473d80118b26d1a86133 schema:name doi
    230 schema:value 10.1186/s12968-016-0320-8
    231 rdf:type schema:PropertyValue
    232 Ndb0abe5280514ec58a8afd729384e74b rdf:first sg:person.015547527234.48
    233 rdf:rest N1785a06a42f14e40bcf9cecf2606ed8e
    234 Ne79ae0e603684bcfbb8da7ff598e5ac1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    235 schema:name Prospective Studies
    236 rdf:type schema:DefinedTerm
    237 Neea84463d01a4925a83806c13639bc90 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    238 schema:name Young Adult
    239 rdf:type schema:DefinedTerm
    240 Nf20d33ea353d4021891715d630eb14c4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    241 schema:name Gadolinium DTPA
    242 rdf:type schema:DefinedTerm
    243 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    244 schema:name Medical and Health Sciences
    245 rdf:type schema:DefinedTerm
    246 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
    247 schema:name Cardiorespiratory Medicine and Haematology
    248 rdf:type schema:DefinedTerm
    249 sg:grant.100067747 http://pending.schema.org/fundedItem sg:pub.10.1186/s12968-016-0320-8
    250 rdf:type schema:MonetaryGrant
    251 sg:grant.7195618 http://pending.schema.org/fundedItem sg:pub.10.1186/s12968-016-0320-8
    252 rdf:type schema:MonetaryGrant
    253 sg:grant.8350505 http://pending.schema.org/fundedItem sg:pub.10.1186/s12968-016-0320-8
    254 rdf:type schema:MonetaryGrant
    255 sg:journal.1030439 schema:issn 1548-7679
    256 1879-2855
    257 schema:name Journal of Cardiovascular Magnetic Resonance
    258 schema:publisher Springer Nature
    259 rdf:type schema:Periodical
    260 sg:person.010153375363.55 schema:affiliation grid-institutes:grid.459864.2
    261 schema:familyName He
    262 schema:givenName Zhuonan
    263 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010153375363.55
    264 rdf:type schema:Person
    265 sg:person.01066244563.44 schema:affiliation grid-institutes:grid.415886.6
    266 schema:familyName Bi
    267 schema:givenName Xiaoming
    268 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066244563.44
    269 rdf:type schema:Person
    270 sg:person.010750755763.16 schema:affiliation grid-institutes:grid.459864.2
    271 schema:familyName Ye
    272 schema:givenName Yufeng
    273 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010750755763.16
    274 rdf:type schema:Person
    275 sg:person.01136172260.58 schema:affiliation grid-institutes:grid.50956.3f
    276 schema:familyName Fan
    277 schema:givenName Zhaoyang
    278 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136172260.58
    279 rdf:type schema:Person
    280 sg:person.01152021525.33 schema:affiliation grid-institutes:grid.50956.3f
    281 schema:familyName Li
    282 schema:givenName Debiao
    283 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152021525.33
    284 rdf:type schema:Person
    285 sg:person.01240364572.83 schema:affiliation grid-institutes:grid.50956.3f
    286 schema:familyName Xie
    287 schema:givenName Guoxi
    288 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240364572.83
    289 rdf:type schema:Person
    290 sg:person.01353064311.62 schema:affiliation grid-institutes:grid.413259.8
    291 schema:familyName Yang
    292 schema:givenName Qi
    293 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01353064311.62
    294 rdf:type schema:Person
    295 sg:person.015547527234.48 schema:affiliation grid-institutes:grid.458489.c
    296 schema:familyName Liu
    297 schema:givenName Xin
    298 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015547527234.48
    299 rdf:type schema:Person
    300 sg:person.016055225063.44 schema:affiliation grid-institutes:grid.411866.c
    301 schema:familyName He
    302 schema:givenName Xueping
    303 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016055225063.44
    304 rdf:type schema:Person
    305 sg:person.016652605463.52 schema:affiliation grid-institutes:grid.459864.2
    306 schema:familyName Liang
    307 schema:givenName Jianke
    308 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016652605463.52
    309 rdf:type schema:Person
    310 sg:person.0607062163.73 schema:affiliation grid-institutes:grid.459864.2
    311 schema:familyName Chen
    312 schema:givenName Hanwei
    313 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0607062163.73
    314 rdf:type schema:Person
    315 sg:person.07356014763.21 schema:affiliation grid-institutes:grid.459864.2
    316 schema:familyName Deng
    317 schema:givenName Wei
    318 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07356014763.21
    319 rdf:type schema:Person
    320 sg:pub.10.1007/s10334-009-0189-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042071819
    321 https://doi.org/10.1007/s10334-009-0189-8
    322 rdf:type schema:CreativeWork
    323 sg:pub.10.1186/1471-2342-5-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048837898
    324 https://doi.org/10.1186/1471-2342-5-6
    325 rdf:type schema:CreativeWork
    326 grid-institutes:grid.411866.c schema:alternateName Guangzhou University of Chinese Medicine, 510405, Guangzhou, Guangdong, China
    327 schema:name Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China
    328 Guangzhou University of Chinese Medicine, 510405, Guangzhou, Guangdong, China
    329 rdf:type schema:Organization
    330 grid-institutes:grid.413259.8 schema:alternateName Department of Radiology, Xuanwu Hospital, 100053, Beijing, China
    331 schema:name Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Pacific Theatres Building, Suite 800, 8700 Beverly Blvd, 90048, Los Angeles, CA, USA
    332 Department of Radiology, Xuanwu Hospital, 100053, Beijing, China
    333 rdf:type schema:Organization
    334 grid-institutes:grid.415886.6 schema:alternateName MR R&D, Siemens Healthcare, 90048, Los Angeles, CA, USA
    335 schema:name MR R&D, Siemens Healthcare, 90048, Los Angeles, CA, USA
    336 rdf:type schema:Organization
    337 grid-institutes:grid.458489.c schema:alternateName Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, CAS, 518055, Guangdong, China
    338 schema:name Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, CAS, 518055, Guangdong, China
    339 rdf:type schema:Organization
    340 grid-institutes:grid.459864.2 schema:alternateName Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China
    341 schema:name Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China
    342 rdf:type schema:Organization
    343 grid-institutes:grid.50956.3f schema:alternateName Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Pacific Theatres Building, Suite 800, 8700 Beverly Blvd, 90048, Los Angeles, CA, USA
    344 schema:name Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Pacific Theatres Building, Suite 800, 8700 Beverly Blvd, 90048, Los Angeles, CA, USA
    345 Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, CAS, 518055, Guangdong, China
    346 rdf:type schema:Organization
     




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


    ...