Cardiovascular magnetic resonance black-blood thrombus imaging for the diagnosis of acute deep vein thrombosis at 1.5 Tesla View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-06-25

AUTHORS

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

ABSTRACT

BackgroundThe aim was to investigate the feasibility of a cardiovascular magnetic resonance (CMR) black-blood thrombus imaging (BBTI) technique, based on delay alternating with nutation for tailored excitation black-blood preparation and a variable flip angle turbo-spin-echo readout, for the diagnosis of acute deep vein thrombosis (DVT) at 1.5 T.MethodsBBTI was conducted in 15 healthy subjects and 30 acute DVT patients. Contrast-enhanced CMR venography (CE-CMRV) was conducted for comparison and only performed in the patients. Apparent contrast-to-noise ratios between the thrombus and the muscle/lumen were calculated to determine whether BBTI could provide an adequate thrombus signal for diagnosis. Two blinded readers assessed the randomized BBTI images from all participants and made independent decisions on the presence or absence of thrombus at the segment level. Images obtained by CE-CMRV were also randomized and assessed by the two readers. Using the consensus CE-CMRV as a reference, the sensitivity, specificity, positive and negative predictive values, and accuracy of BBTI, as well as its diagnostic agreement with CE-CMRV, were calculated. Additionally, diagnostic confidence and interobserver diagnostic agreement were evaluated.ResultsThe thrombi in the acute phase exhibited iso- or hyperintense signals on the BBTI images. All the healthy subjects were correctly identified from the participants based on the segment level. The diagnostic confidence of BBTI was comparable to that of CE-CMRV (3.69 ± 0.52 vs. 3.70 ± 0.47). High overall sensitivity (95.2%), SP (98.6%), positive predictive value (96.0%), negative predictive value (98.3%), and accuracy (97.7%), as well as excellent diagnostic and interobserver agreements, were achieved using BBTI.ConclusionBBTI is a reliable, contrast-free technique for the diagnosis of acute DVT at 1.5 T. More... »

PAGES

42

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12968-018-0459-6

DOI

http://dx.doi.org/10.1186/s12968-018-0459-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104981088

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29936910


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": "Aged, 80 and over", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Case-Control Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Feasibility Studies", 
        "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": "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Medical Imaging Institute of Panyu, 511400, Guangzhou, Guangdong, China", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China", 
            "Medical Imaging Institute of Panyu, 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": "Medical Imaging Institute of Panyu, 511400, Guangzhou, Guangdong, China", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China", 
            "Medical Imaging Institute of Panyu, 511400, 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 Biomedical Engineering of Basic Medical School, Guangzhou Medical University, 511436, Guangzhou, Guangdong, China", 
          "id": "http://www.grid.ac/institutes/grid.410737.6", 
          "name": [
            "The Sixth Affiliated Hospital, Guangzhou Medical University, Xinzao, Panyu District, 511518, Qingyuan, Guangdong, China", 
            "Department of Biomedical Engineering of Basic Medical School, Guangzhou Medical University, 511436, Guangzhou, Guangdong, China"
          ], 
          "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": "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": "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, 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": "Liu", 
        "givenName": "Dexiang", 
        "id": "sg:person.014452134371.24", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014452134371.24"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 90048, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.50956.3f", 
          "name": [
            "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 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": "Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, Guangdong, China", 
          "id": "http://www.grid.ac/institutes/grid.458489.c", 
          "name": [
            "Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, 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, 90048, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.50956.3f", 
          "name": [
            "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 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.10.1007/s00330-016-4555-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053118496", 
          "https://doi.org/10.1007/s00330-016-4555-4"
        ], 
        "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"
      }, 
      {
        "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/s00270-013-0747-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034548551", 
          "https://doi.org/10.1007/s00270-013-0747-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12968-016-0320-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014715046", 
          "https://doi.org/10.1186/s12968-016-0320-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003300000586", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008539791", 
          "https://doi.org/10.1007/s003300000586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-006-0178-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043641001", 
          "https://doi.org/10.1007/s00330-006-0178-5"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-06-25", 
    "datePublishedReg": "2018-06-25", 
    "description": "BackgroundThe aim was to investigate the feasibility of a cardiovascular magnetic resonance (CMR) black-blood thrombus imaging (BBTI) technique, based on delay alternating with nutation for tailored excitation black-blood preparation and a variable flip angle turbo-spin-echo readout, for the diagnosis of acute deep vein thrombosis (DVT) at 1.5\u00a0T.MethodsBBTI was conducted in 15 healthy subjects and 30 acute DVT patients. Contrast-enhanced CMR venography (CE-CMRV) was conducted for comparison and only performed in the patients. Apparent contrast-to-noise ratios between the thrombus and the muscle/lumen were calculated to determine whether BBTI could provide an adequate thrombus signal for diagnosis. Two blinded readers assessed the randomized BBTI images from all participants and made independent decisions on the presence or absence of thrombus at the segment level. Images obtained by CE-CMRV were also randomized and assessed by the two readers. Using the consensus CE-CMRV as a reference, the sensitivity, specificity, positive and negative predictive values, and accuracy of BBTI, as well as its diagnostic agreement with CE-CMRV, were calculated. Additionally, diagnostic confidence and interobserver diagnostic agreement were evaluated.ResultsThe thrombi in the acute phase exhibited iso- or hyperintense signals on the BBTI images. All the healthy subjects were correctly identified from the participants based on the segment level. The diagnostic confidence of BBTI was comparable to that of CE-CMRV (3.69\u2009\u00b1\u20090.52 vs. 3.70\u2009\u00b1\u20090.47). High overall sensitivity (95.2%), SP (98.6%), positive predictive value (96.0%), negative predictive value (98.3%), and accuracy (97.7%), as well as excellent diagnostic and interobserver agreements, were achieved using BBTI.ConclusionBBTI is a reliable, contrast-free technique for the diagnosis of acute DVT at 1.5\u00a0T.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s12968-018-0459-6", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.8366653", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8350505", 
        "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": "20"
      }
    ], 
    "keywords": [
      "acute deep vein thrombosis", 
      "deep vein thrombosis", 
      "negative predictive value", 
      "predictive value", 
      "vein thrombosis", 
      "healthy subjects", 
      "diagnostic agreement", 
      "diagnostic confidence", 
      "acute DVT patients", 
      "absence of thrombus", 
      "positive predictive value", 
      "DVT patients", 
      "acute phase", 
      "hyperintense signal", 
      "higher overall sensitivity", 
      "thrombus", 
      "overall sensitivity", 
      "interobserver agreement", 
      "blinded readers", 
      "diagnosis", 
      "black-blood preparation", 
      "thrombosis", 
      "patients", 
      "imaging techniques", 
      "subjects", 
      "participants", 
      "venography", 
      "segment level", 
      "BBTI", 
      "levels", 
      "lumen", 
      "variable flip angle", 
      "sensitivity", 
      "aim", 
      "apparent contrast", 
      "specificity", 
      "absence", 
      "flip angle", 
      "confidence", 
      "contrast", 
      "echo readout", 
      "presence", 
      "independent decisions", 
      "values", 
      "Tesla", 
      "feasibility", 
      "delay", 
      "ratio", 
      "technique", 
      "preparation", 
      "comparison", 
      "ISO", 
      "images", 
      "decisions", 
      "signals", 
      "reference", 
      "readers", 
      "accuracy", 
      "phase", 
      "readout", 
      "agreement", 
      "sp", 
      "angle", 
      "noise ratio", 
      "nutation"
    ], 
    "name": "Cardiovascular magnetic resonance black-blood thrombus imaging for the diagnosis of acute deep vein thrombosis at 1.5 Tesla", 
    "pagination": "42", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1104981088"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12968-018-0459-6"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29936910"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12968-018-0459-6", 
      "https://app.dimensions.ai/details/publication/pub.1104981088"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-10-01T06:44", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_768.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s12968-018-0459-6"
  }
]
 

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/s12968-018-0459-6'

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/s12968-018-0459-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12968-018-0459-6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12968-018-0459-6'


 

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

306 TRIPLES      21 PREDICATES      113 URIs      98 LITERALS      23 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12968-018-0459-6 schema:about N0f8dc6d879b349d7b22b749fbd83e552
2 N1ab02a0083da45c3885b80ccd289c9b5
3 N1cd900c2df45426eaa0a7387c7630139
4 N241408761a0148059e04d79dd621ba54
5 N271cc06b56c842a09a696b1320885033
6 N2f726743632a48f8bd974d8325d9efdd
7 N3119aec55f794208829964a84975296e
8 N6fe13ab97eac471ba60f667e56f9a9b8
9 N757d49a2bc804da187d3933e0a239144
10 N77125c64798045d89e1954daed833399
11 N8af032a25db14823a7bb2aab1bb5548d
12 Nbf094b2edbf14b98b72fafa055319787
13 Nc2a3e2cbc3594dfaa47fbe219cc5f42b
14 Nc84b9a1b0adf49c1a9f5c94899855dc0
15 Ne984d922fbf04a69aa104dbe3ba0ef57
16 Nf92d5c6ebfd541a39e8ece3e1f9574cc
17 anzsrc-for:11
18 anzsrc-for:1102
19 schema:author Nc1c9c4c43f5a43cfb56442da087d1b36
20 schema:citation sg:pub.10.1007/s00270-013-0747-3
21 sg:pub.10.1007/s00330-006-0178-5
22 sg:pub.10.1007/s00330-016-4555-4
23 sg:pub.10.1007/s003300000586
24 sg:pub.10.1007/s10334-009-0189-8
25 sg:pub.10.1186/1471-2342-5-6
26 sg:pub.10.1186/s12968-016-0320-8
27 schema:datePublished 2018-06-25
28 schema:datePublishedReg 2018-06-25
29 schema:description BackgroundThe aim was to investigate the feasibility of a cardiovascular magnetic resonance (CMR) black-blood thrombus imaging (BBTI) technique, based on delay alternating with nutation for tailored excitation black-blood preparation and a variable flip angle turbo-spin-echo readout, for the diagnosis of acute deep vein thrombosis (DVT) at 1.5 T.MethodsBBTI was conducted in 15 healthy subjects and 30 acute DVT patients. Contrast-enhanced CMR venography (CE-CMRV) was conducted for comparison and only performed in the patients. Apparent contrast-to-noise ratios between the thrombus and the muscle/lumen were calculated to determine whether BBTI could provide an adequate thrombus signal for diagnosis. Two blinded readers assessed the randomized BBTI images from all participants and made independent decisions on the presence or absence of thrombus at the segment level. Images obtained by CE-CMRV were also randomized and assessed by the two readers. Using the consensus CE-CMRV as a reference, the sensitivity, specificity, positive and negative predictive values, and accuracy of BBTI, as well as its diagnostic agreement with CE-CMRV, were calculated. Additionally, diagnostic confidence and interobserver diagnostic agreement were evaluated.ResultsThe thrombi in the acute phase exhibited iso- or hyperintense signals on the BBTI images. All the healthy subjects were correctly identified from the participants based on the segment level. The diagnostic confidence of BBTI was comparable to that of CE-CMRV (3.69 ± 0.52 vs. 3.70 ± 0.47). High overall sensitivity (95.2%), SP (98.6%), positive predictive value (96.0%), negative predictive value (98.3%), and accuracy (97.7%), as well as excellent diagnostic and interobserver agreements, were achieved using BBTI.ConclusionBBTI is a reliable, contrast-free technique for the diagnosis of acute DVT at 1.5 T.
30 schema:genre article
31 schema:isAccessibleForFree true
32 schema:isPartOf N0d474731192f4af090c00673ab74e478
33 N1be7b02ae6864ae09aea572903f40c66
34 sg:journal.1030439
35 schema:keywords BBTI
36 DVT patients
37 ISO
38 Tesla
39 absence
40 absence of thrombus
41 accuracy
42 acute DVT patients
43 acute deep vein thrombosis
44 acute phase
45 agreement
46 aim
47 angle
48 apparent contrast
49 black-blood preparation
50 blinded readers
51 comparison
52 confidence
53 contrast
54 decisions
55 deep vein thrombosis
56 delay
57 diagnosis
58 diagnostic agreement
59 diagnostic confidence
60 echo readout
61 feasibility
62 flip angle
63 healthy subjects
64 higher overall sensitivity
65 hyperintense signal
66 images
67 imaging techniques
68 independent decisions
69 interobserver agreement
70 levels
71 lumen
72 negative predictive value
73 noise ratio
74 nutation
75 overall sensitivity
76 participants
77 patients
78 phase
79 positive predictive value
80 predictive value
81 preparation
82 presence
83 ratio
84 readers
85 readout
86 reference
87 segment level
88 sensitivity
89 signals
90 sp
91 specificity
92 subjects
93 technique
94 thrombosis
95 thrombus
96 values
97 variable flip angle
98 vein thrombosis
99 venography
100 schema:name Cardiovascular magnetic resonance black-blood thrombus imaging for the diagnosis of acute deep vein thrombosis at 1.5 Tesla
101 schema:pagination 42
102 schema:productId N9045de6396c5471c9e06565d0dfbbec0
103 N91f4fc3009e94fed8ee3ec895f5e1dba
104 Nadd5c769da4f4468bea6bf1992761d0d
105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104981088
106 https://doi.org/10.1186/s12968-018-0459-6
107 schema:sdDatePublished 2022-10-01T06:44
108 schema:sdLicense https://scigraph.springernature.com/explorer/license/
109 schema:sdPublisher Nc5d8462b806e40c78b9f1d1e0002db02
110 schema:url https://doi.org/10.1186/s12968-018-0459-6
111 sgo:license sg:explorer/license/
112 sgo:sdDataset articles
113 rdf:type schema:ScholarlyArticle
114 N06c7b816dec24c719620fcb11ba53b7d rdf:first sg:person.014452134371.24
115 rdf:rest N94c0c709a50d4ff490a1e0c821ff3f1e
116 N0d474731192f4af090c00673ab74e478 schema:volumeNumber 20
117 rdf:type schema:PublicationVolume
118 N0f8dc6d879b349d7b22b749fbd83e552 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
119 schema:name Venous Thrombosis
120 rdf:type schema:DefinedTerm
121 N1ab02a0083da45c3885b80ccd289c9b5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
122 schema:name Case-Control Studies
123 rdf:type schema:DefinedTerm
124 N1be7b02ae6864ae09aea572903f40c66 schema:issueNumber 1
125 rdf:type schema:PublicationIssue
126 N1cd900c2df45426eaa0a7387c7630139 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
127 schema:name Female
128 rdf:type schema:DefinedTerm
129 N241408761a0148059e04d79dd621ba54 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
130 schema:name Male
131 rdf:type schema:DefinedTerm
132 N271cc06b56c842a09a696b1320885033 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
133 schema:name Humans
134 rdf:type schema:DefinedTerm
135 N28d52e22c9144f5cae6513536422ec26 rdf:first sg:person.01136172260.58
136 rdf:rest rdf:nil
137 N2ec597bf7c0f4459a669bb74af9ec3e7 rdf:first sg:person.07356014763.21
138 rdf:rest N4ffba0254e944bd58d96f8c15c17869c
139 N2f726743632a48f8bd974d8325d9efdd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
140 schema:name Prospective Studies
141 rdf:type schema:DefinedTerm
142 N3119aec55f794208829964a84975296e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Aged, 80 and over
144 rdf:type schema:DefinedTerm
145 N42c1387e2a184646861195473ffd6a30 rdf:first sg:person.01240364572.83
146 rdf:rest N6110c76a82eb43c09dd261c89b1ea899
147 N4ffba0254e944bd58d96f8c15c17869c rdf:first sg:person.010153375363.55
148 rdf:rest N06c7b816dec24c719620fcb11ba53b7d
149 N6110c76a82eb43c09dd261c89b1ea899 rdf:first sg:person.016652605463.52
150 rdf:rest Naefdd4f3916846b583a0d7c85f3cf5ca
151 N6fe13ab97eac471ba60f667e56f9a9b8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
152 schema:name Feasibility Studies
153 rdf:type schema:DefinedTerm
154 N757d49a2bc804da187d3933e0a239144 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
155 schema:name Magnetic Resonance Angiography
156 rdf:type schema:DefinedTerm
157 N77125c64798045d89e1954daed833399 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
158 schema:name Aged
159 rdf:type schema:DefinedTerm
160 N8af032a25db14823a7bb2aab1bb5548d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
161 schema:name Phlebography
162 rdf:type schema:DefinedTerm
163 N9045de6396c5471c9e06565d0dfbbec0 schema:name pubmed_id
164 schema:value 29936910
165 rdf:type schema:PropertyValue
166 N91f4fc3009e94fed8ee3ec895f5e1dba schema:name dimensions_id
167 schema:value pub.1104981088
168 rdf:type schema:PropertyValue
169 N92c96f94169f433180bd0127649b55e8 rdf:first sg:person.015547527234.48
170 rdf:rest N28d52e22c9144f5cae6513536422ec26
171 N94c0c709a50d4ff490a1e0c821ff3f1e rdf:first sg:person.01152021525.33
172 rdf:rest N92c96f94169f433180bd0127649b55e8
173 Nadd5c769da4f4468bea6bf1992761d0d schema:name doi
174 schema:value 10.1186/s12968-018-0459-6
175 rdf:type schema:PropertyValue
176 Naefdd4f3916846b583a0d7c85f3cf5ca rdf:first sg:person.010750755763.16
177 rdf:rest N2ec597bf7c0f4459a669bb74af9ec3e7
178 Nbf094b2edbf14b98b72fafa055319787 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
179 schema:name Middle Aged
180 rdf:type schema:DefinedTerm
181 Nc1c9c4c43f5a43cfb56442da087d1b36 rdf:first sg:person.0607062163.73
182 rdf:rest Ne1df28f4edd44928b88d33ff4ad016a9
183 Nc2a3e2cbc3594dfaa47fbe219cc5f42b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
184 schema:name Adult
185 rdf:type schema:DefinedTerm
186 Nc5d8462b806e40c78b9f1d1e0002db02 schema:name Springer Nature - SN SciGraph project
187 rdf:type schema:Organization
188 Nc84b9a1b0adf49c1a9f5c94899855dc0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
189 schema:name Predictive Value of Tests
190 rdf:type schema:DefinedTerm
191 Ne1df28f4edd44928b88d33ff4ad016a9 rdf:first sg:person.016055225063.44
192 rdf:rest N42c1387e2a184646861195473ffd6a30
193 Ne984d922fbf04a69aa104dbe3ba0ef57 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
194 schema:name Observer Variation
195 rdf:type schema:DefinedTerm
196 Nf92d5c6ebfd541a39e8ece3e1f9574cc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
197 schema:name Reproducibility of Results
198 rdf:type schema:DefinedTerm
199 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
200 schema:name Medical and Health Sciences
201 rdf:type schema:DefinedTerm
202 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
203 schema:name Cardiorespiratory Medicine and Haematology
204 rdf:type schema:DefinedTerm
205 sg:grant.8350505 http://pending.schema.org/fundedItem sg:pub.10.1186/s12968-018-0459-6
206 rdf:type schema:MonetaryGrant
207 sg:grant.8366653 http://pending.schema.org/fundedItem sg:pub.10.1186/s12968-018-0459-6
208 rdf:type schema:MonetaryGrant
209 sg:journal.1030439 schema:issn 1548-7679
210 1879-2855
211 schema:name Journal of Cardiovascular Magnetic Resonance
212 schema:publisher Springer Nature
213 rdf:type schema:Periodical
214 sg:person.010153375363.55 schema:affiliation grid-institutes:grid.459864.2
215 schema:familyName He
216 schema:givenName Zhuonan
217 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010153375363.55
218 rdf:type schema:Person
219 sg:person.010750755763.16 schema:affiliation grid-institutes:grid.459864.2
220 schema:familyName Ye
221 schema:givenName Yufeng
222 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010750755763.16
223 rdf:type schema:Person
224 sg:person.01136172260.58 schema:affiliation grid-institutes:grid.50956.3f
225 schema:familyName Fan
226 schema:givenName Zhaoyang
227 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136172260.58
228 rdf:type schema:Person
229 sg:person.01152021525.33 schema:affiliation grid-institutes:grid.50956.3f
230 schema:familyName Li
231 schema:givenName Debiao
232 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152021525.33
233 rdf:type schema:Person
234 sg:person.01240364572.83 schema:affiliation grid-institutes:grid.410737.6
235 schema:familyName Xie
236 schema:givenName Guoxi
237 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240364572.83
238 rdf:type schema:Person
239 sg:person.014452134371.24 schema:affiliation grid-institutes:grid.459864.2
240 schema:familyName Liu
241 schema:givenName Dexiang
242 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014452134371.24
243 rdf:type schema:Person
244 sg:person.015547527234.48 schema:affiliation grid-institutes:grid.458489.c
245 schema:familyName Liu
246 schema:givenName Xin
247 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015547527234.48
248 rdf:type schema:Person
249 sg:person.016055225063.44 schema:affiliation grid-institutes:None
250 schema:familyName He
251 schema:givenName Xueping
252 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016055225063.44
253 rdf:type schema:Person
254 sg:person.016652605463.52 schema:affiliation grid-institutes:grid.459864.2
255 schema:familyName Liang
256 schema:givenName Jianke
257 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016652605463.52
258 rdf:type schema:Person
259 sg:person.0607062163.73 schema:affiliation grid-institutes:None
260 schema:familyName Chen
261 schema:givenName Hanwei
262 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0607062163.73
263 rdf:type schema:Person
264 sg:person.07356014763.21 schema:affiliation grid-institutes:grid.459864.2
265 schema:familyName Deng
266 schema:givenName Wei
267 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07356014763.21
268 rdf:type schema:Person
269 sg:pub.10.1007/s00270-013-0747-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034548551
270 https://doi.org/10.1007/s00270-013-0747-3
271 rdf:type schema:CreativeWork
272 sg:pub.10.1007/s00330-006-0178-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043641001
273 https://doi.org/10.1007/s00330-006-0178-5
274 rdf:type schema:CreativeWork
275 sg:pub.10.1007/s00330-016-4555-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053118496
276 https://doi.org/10.1007/s00330-016-4555-4
277 rdf:type schema:CreativeWork
278 sg:pub.10.1007/s003300000586 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008539791
279 https://doi.org/10.1007/s003300000586
280 rdf:type schema:CreativeWork
281 sg:pub.10.1007/s10334-009-0189-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042071819
282 https://doi.org/10.1007/s10334-009-0189-8
283 rdf:type schema:CreativeWork
284 sg:pub.10.1186/1471-2342-5-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048837898
285 https://doi.org/10.1186/1471-2342-5-6
286 rdf:type schema:CreativeWork
287 sg:pub.10.1186/s12968-016-0320-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014715046
288 https://doi.org/10.1186/s12968-016-0320-8
289 rdf:type schema:CreativeWork
290 grid-institutes:None schema:alternateName Medical Imaging Institute of Panyu, 511400, Guangzhou, Guangdong, China
291 schema:name Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China
292 Medical Imaging Institute of Panyu, 511400, Guangzhou, Guangdong, China
293 rdf:type schema:Organization
294 grid-institutes:grid.410737.6 schema:alternateName Department of Biomedical Engineering of Basic Medical School, Guangzhou Medical University, 511436, Guangzhou, Guangdong, China
295 schema:name Department of Biomedical Engineering of Basic Medical School, Guangzhou Medical University, 511436, Guangzhou, Guangdong, China
296 The Sixth Affiliated Hospital, Guangzhou Medical University, Xinzao, Panyu District, 511518, Qingyuan, Guangdong, China
297 rdf:type schema:Organization
298 grid-institutes:grid.458489.c schema:alternateName Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, Guangdong, China
299 schema:name Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, Guangdong, China
300 rdf:type schema:Organization
301 grid-institutes:grid.459864.2 schema:alternateName Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China
302 schema:name Department of Radiology, Guangzhou Panyu Central Hospital, 511400, Guangzhou, Guangdong, China
303 rdf:type schema:Organization
304 grid-institutes:grid.50956.3f schema:alternateName Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 90048, Los Angeles, CA, USA
305 schema:name Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 90048, Los Angeles, CA, USA
306 rdf:type schema:Organization
 




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


...