Assessment of carotid atherosclerotic disease using three-dimensional cardiovascular magnetic resonance vessel wall imaging: comparison with digital subtraction angiography View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-03-05

AUTHORS

Zhenjia Wang, Mi Lu, Wen Liu, Tiejin Zheng, Debiao Li, Wei Yu, Zhaoyang Fan

ABSTRACT

BackgroundA three-dimensional (3D) cardiovascular magnetic resonance (CMR) vessel wall imaging (VWI) technique based on 3D T1 weighted (T1w) Sampling Perfection with Application-optimized Contrast using different flip angle Evolutions (SPACE) has recently been used as a promising CMR imaging modality for evaluating extra-cranial and intra-cranial vessel walls. However, this technique is yet to be validated against the current diagnostic imaging standard. We therefore aimed to evaluate the diagnostic performance of 3D CMR VWI in characterizing carotid disease using intra-arterial digital subtraction angiography (DSA) as a reference.MethodsConsecutive patients with at least unilateral > 50% carotid stenosis on ultrasound were scheduled to undergo interventional therapy were invited to participate. The following metrics were measured using 3D CMR VWI and DSA: lumen diameter of the common carotid artery (CCA) and segments C1–C7, stenosis diameter, reference diameter, lesion length, stenosis degree, and ulceration. We assessed the diagnostic sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve of 3D CMR VWI, and used Cohen’s kappa, the intraclass correlation coefficient (ICC), and Bland-Altman analyses to assess the diagnostic agreement between 3D CMR VWI and DSA.ResultsThe ICC (all ICCs ≥0.96) and Bland-Altman plots indicated excellent inter-reader agreement in all individual morphologic measurements by 3D CMR VWI. Excellent agreement in all individual morphologic measurements were also found between 3D CMR VWI and DSA. In addition, 3D CMR VWI had high sensitivity (98.4, 97.4, 80.0, 100.0%), specificity (100.0, 94.5, 99.1, 98.0%), and Cohen’s kappa (0.99, 0.89, 0.84, 0.96) for detecting stenosis > 50%, stenosis > 70%, ulceration, and total occlusion, respectively, using DSA as the standard. The AUC of 3D CMR VWI for predicting stenosis > 50 and > 70% were 0.998 and 0.999, respectively.ConclusionsThe 3D CMR VWI technique enables accurate diagnosis and luminal feature assessment of carotid artery atherosclerosis, suggesting that this imaging modality may be useful for routine imaging workups and provide comprehensive information for both the vessel wall and lumen. More... »

PAGES

18

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12968-020-0604-x

DOI

http://dx.doi.org/10.1186/s12968-020-0604-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1125401947

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32131854


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": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Angiography, Digital Subtraction", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Carotid Stenosis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Imaging, Three-Dimensional", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "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": "Plaque, Atherosclerotic", 
        "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": "Severity of Illness Index", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, No. 23, Back Road of Art Gallery, 100010, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2 Anzhen Road, 100029, Beijing, China", 
            "Department of Radiology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, No. 23, Back Road of Art Gallery, 100010, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Zhenjia", 
        "id": "sg:person.013371766555.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013371766555.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Otolaryngology Head and Neck Surgery, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, 100029, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Otolaryngology Head and Neck Surgery, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, 100029, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lu", 
        "givenName": "Mi", 
        "id": "sg:person.011173575413.11", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011173575413.11"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2 Anzhen Road, 100029, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2 Anzhen Road, 100029, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Wen", 
        "id": "sg:person.012016701452.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012016701452.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Neurosurgery, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, 100029, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Neurosurgery, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, 100029, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zheng", 
        "givenName": "Tiejin", 
        "id": "sg:person.013205052263.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013205052263.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Bioengineering, University of California, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.19006.3e", 
          "name": [
            "Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., PACT 400, 90048, 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"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2 Anzhen Road, 100029, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2 Anzhen Road, 100029, 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": "Department of Bioengineering, University of California, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.19006.3e", 
          "name": [
            "Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., PACT 400, 90048, Los Angeles, CA, USA", 
            "Department of Bioengineering, University of California, 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.10.1186/1532-429x-11-41", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003933091", 
          "https://doi.org/10.1186/1532-429x-11-41"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep33246", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008183501", 
          "https://doi.org/10.1038/srep33246"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1532-429x-10-31", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042265023", 
          "https://doi.org/10.1186/1532-429x-10-31"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12968-018-0453-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104560508", 
          "https://doi.org/10.1186/s12968-018-0453-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-017-1228-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091201460", 
          "https://doi.org/10.1007/s10554-017-1228-6"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2020-03-05", 
    "datePublishedReg": "2020-03-05", 
    "description": "BackgroundA three-dimensional (3D)\u00a0cardiovascular magnetic resonance (CMR)\u00a0vessel wall imaging (VWI) technique based on 3D T1 weighted\u00a0(T1w) Sampling Perfection with Application-optimized Contrast using different flip angle Evolutions (SPACE) has recently been used as a promising CMR imaging modality for evaluating extra-cranial and intra-cranial vessel walls. However, this technique is yet to be validated against the current diagnostic imaging standard. We therefore aimed to evaluate the diagnostic performance of 3D CMR VWI in characterizing carotid disease using intra-arterial digital subtraction angiography (DSA) as a reference.MethodsConsecutive patients with at least unilateral >\u200950% carotid stenosis on ultrasound were scheduled to undergo interventional therapy were invited to participate. The following metrics were measured using 3D CMR VWI and DSA: lumen diameter of the common carotid artery (CCA) and segments C1\u2013C7, stenosis diameter, reference diameter, lesion length, stenosis degree, and ulceration. We assessed the diagnostic sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve of 3D CMR VWI, and used Cohen\u2019s kappa, the intraclass correlation coefficient (ICC), and Bland-Altman analyses to assess the diagnostic agreement between 3D CMR VWI and DSA.ResultsThe ICC (all ICCs \u22650.96) and Bland-Altman plots indicated excellent inter-reader agreement in all individual morphologic measurements by 3D CMR VWI. Excellent agreement in all individual morphologic measurements were also found between 3D CMR VWI and DSA. In addition, 3D CMR VWI had high sensitivity (98.4, 97.4, 80.0, 100.0%), specificity (100.0, 94.5, 99.1, 98.0%), and Cohen\u2019s kappa (0.99, 0.89, 0.84, 0.96) for detecting stenosis >\u200950%, stenosis >\u200970%, ulceration, and total occlusion, respectively, using DSA as the standard. The AUC of 3D CMR VWI for predicting stenosis >\u200950 and\u2009>\u200970% were 0.998 and 0.999, respectively.ConclusionsThe 3D CMR VWI technique enables accurate diagnosis and luminal feature assessment of carotid artery atherosclerosis, suggesting that this imaging modality may be useful for routine imaging workups and provide comprehensive information for both the vessel wall and lumen.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s12968-020-0604-x", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.8388914", 
        "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": "22"
      }
    ], 
    "keywords": [
      "digital subtraction angiography", 
      "cardiovascular magnetic resonance", 
      "excellent agreement", 
      "common carotid artery", 
      "intraclass correlation coefficient", 
      "diameter", 
      "wall imaging", 
      "high sensitivity", 
      "wall", 
      "angle evolution", 
      "subtraction angiography", 
      "measurements", 
      "agreement", 
      "technique", 
      "carotid artery atherosclerosis", 
      "intra-arterial digital subtraction angiography", 
      "vessel wall", 
      "carotid atherosclerotic disease", 
      "morphologic measurements", 
      "Cohen's kappa", 
      "magnetic resonance vessel wall imaging", 
      "vessel wall imaging techniques", 
      "excellent inter-reader agreement", 
      "inter-reader agreement", 
      "vessel wall imaging", 
      "performance", 
      "MethodsConsecutive patients", 
      "artery atherosclerosis", 
      "carotid disease", 
      "Bland-Altman plots", 
      "total occlusion", 
      "imaging workup", 
      "atherosclerotic disease", 
      "carotid stenosis", 
      "interventional therapy", 
      "Bland-Altman analysis", 
      "carotid artery", 
      "stenosis diameter", 
      "coefficient", 
      "stenosis", 
      "reference diameter", 
      "lumen diameter", 
      "stenosis degree", 
      "accurate diagnosis", 
      "diagnostic agreement", 
      "lesion length", 
      "diagnostic sensitivity", 
      "accuracy", 
      "diagnostic performance", 
      "imaging modalities", 
      "imaging techniques", 
      "feature assessment", 
      "receiver", 
      "characteristic curve", 
      "ulceration", 
      "imaging standard", 
      "angiography", 
      "disease", 
      "kappa", 
      "curves", 
      "sensitivity", 
      "different flip angle evolutions", 
      "standards", 
      "magnetic resonance", 
      "application-optimized contrasts", 
      "modalities", 
      "VWI", 
      "sampling perfection", 
      "comprehensive information", 
      "resonance", 
      "perfection", 
      "comparison", 
      "length", 
      "reference", 
      "correlation coefficient", 
      "patients", 
      "artery", 
      "atherosclerosis", 
      "therapy", 
      "addition", 
      "workup", 
      "evolution", 
      "specificity", 
      "diagnosis", 
      "occlusion", 
      "AUC", 
      "assessment", 
      "ultrasound", 
      "analysis", 
      "plots", 
      "lumen", 
      "degree", 
      "C1-C7", 
      "imaging", 
      "metrics", 
      "T1", 
      "information", 
      "contrast"
    ], 
    "name": "Assessment of carotid atherosclerotic disease using three-dimensional cardiovascular magnetic resonance vessel wall imaging: comparison with digital subtraction angiography", 
    "pagination": "18", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1125401947"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12968-020-0604-x"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "32131854"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12968-020-0604-x", 
      "https://app.dimensions.ai/details/publication/pub.1125401947"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-12-01T06:41", 
    "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_866.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s12968-020-0604-x"
  }
]
 

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/s12968-020-0604-x'

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/s12968-020-0604-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12968-020-0604-x'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12968-020-0604-x'


 

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

289 TRIPLES      21 PREDICATES      142 URIs      129 LITERALS      21 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12968-020-0604-x schema:about N076391da7f6a4f1bbb2d2a587cecdd21
2 N0be9f1c673ac4b349e211292e105f58c
3 N12b3b0ddb3d843119dd98f769eacf07f
4 N15958acc541b492e953490c27eed731e
5 N1904af8f53af4b6d92163a0af1949c8f
6 N2135f9b50e764a8a84aff20d1f9d8cb5
7 N3582fbcf465444d7bddebd81802775d2
8 N3948c822faaf45d187f0f52bd664fd41
9 N52498afbd7954e1086101d2889532e2a
10 N58f2d5b7bae84ada968017869bfa8a3c
11 N8133155be04a496eb7e0803f21fa1efe
12 N8f6912734d6348a38a9ed64f8ca7078b
13 Nd31493017138465ea6b47b26100c0a92
14 Ne7977103ba3a4a08b053a2898205c77b
15 anzsrc-for:11
16 anzsrc-for:1102
17 schema:author N8dd2a586045e4e3a88ca545358568f63
18 schema:citation sg:pub.10.1007/s10554-017-1228-6
19 sg:pub.10.1038/srep33246
20 sg:pub.10.1186/1532-429x-10-31
21 sg:pub.10.1186/1532-429x-11-41
22 sg:pub.10.1186/s12968-018-0453-z
23 schema:datePublished 2020-03-05
24 schema:datePublishedReg 2020-03-05
25 schema:description BackgroundA three-dimensional (3D) cardiovascular magnetic resonance (CMR) vessel wall imaging (VWI) technique based on 3D T1 weighted (T1w) Sampling Perfection with Application-optimized Contrast using different flip angle Evolutions (SPACE) has recently been used as a promising CMR imaging modality for evaluating extra-cranial and intra-cranial vessel walls. However, this technique is yet to be validated against the current diagnostic imaging standard. We therefore aimed to evaluate the diagnostic performance of 3D CMR VWI in characterizing carotid disease using intra-arterial digital subtraction angiography (DSA) as a reference.MethodsConsecutive patients with at least unilateral > 50% carotid stenosis on ultrasound were scheduled to undergo interventional therapy were invited to participate. The following metrics were measured using 3D CMR VWI and DSA: lumen diameter of the common carotid artery (CCA) and segments C1–C7, stenosis diameter, reference diameter, lesion length, stenosis degree, and ulceration. We assessed the diagnostic sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve of 3D CMR VWI, and used Cohen’s kappa, the intraclass correlation coefficient (ICC), and Bland-Altman analyses to assess the diagnostic agreement between 3D CMR VWI and DSA.ResultsThe ICC (all ICCs ≥0.96) and Bland-Altman plots indicated excellent inter-reader agreement in all individual morphologic measurements by 3D CMR VWI. Excellent agreement in all individual morphologic measurements were also found between 3D CMR VWI and DSA. In addition, 3D CMR VWI had high sensitivity (98.4, 97.4, 80.0, 100.0%), specificity (100.0, 94.5, 99.1, 98.0%), and Cohen’s kappa (0.99, 0.89, 0.84, 0.96) for detecting stenosis > 50%, stenosis > 70%, ulceration, and total occlusion, respectively, using DSA as the standard. The AUC of 3D CMR VWI for predicting stenosis > 50 and > 70% were 0.998 and 0.999, respectively.ConclusionsThe 3D CMR VWI technique enables accurate diagnosis and luminal feature assessment of carotid artery atherosclerosis, suggesting that this imaging modality may be useful for routine imaging workups and provide comprehensive information for both the vessel wall and lumen.
26 schema:genre article
27 schema:isAccessibleForFree true
28 schema:isPartOf N487a9c24250e43f292b70d2dd6098746
29 N537833375b3c43cd889138f855f1e962
30 sg:journal.1030439
31 schema:keywords AUC
32 Bland-Altman analysis
33 Bland-Altman plots
34 C1-C7
35 Cohen's kappa
36 MethodsConsecutive patients
37 T1
38 VWI
39 accuracy
40 accurate diagnosis
41 addition
42 agreement
43 analysis
44 angiography
45 angle evolution
46 application-optimized contrasts
47 artery
48 artery atherosclerosis
49 assessment
50 atherosclerosis
51 atherosclerotic disease
52 cardiovascular magnetic resonance
53 carotid artery
54 carotid artery atherosclerosis
55 carotid atherosclerotic disease
56 carotid disease
57 carotid stenosis
58 characteristic curve
59 coefficient
60 common carotid artery
61 comparison
62 comprehensive information
63 contrast
64 correlation coefficient
65 curves
66 degree
67 diagnosis
68 diagnostic agreement
69 diagnostic performance
70 diagnostic sensitivity
71 diameter
72 different flip angle evolutions
73 digital subtraction angiography
74 disease
75 evolution
76 excellent agreement
77 excellent inter-reader agreement
78 feature assessment
79 high sensitivity
80 imaging
81 imaging modalities
82 imaging standard
83 imaging techniques
84 imaging workup
85 information
86 inter-reader agreement
87 interventional therapy
88 intra-arterial digital subtraction angiography
89 intraclass correlation coefficient
90 kappa
91 length
92 lesion length
93 lumen
94 lumen diameter
95 magnetic resonance
96 magnetic resonance vessel wall imaging
97 measurements
98 metrics
99 modalities
100 morphologic measurements
101 occlusion
102 patients
103 perfection
104 performance
105 plots
106 receiver
107 reference
108 reference diameter
109 resonance
110 sampling perfection
111 sensitivity
112 specificity
113 standards
114 stenosis
115 stenosis degree
116 stenosis diameter
117 subtraction angiography
118 technique
119 therapy
120 total occlusion
121 ulceration
122 ultrasound
123 vessel wall
124 vessel wall imaging
125 vessel wall imaging techniques
126 wall
127 wall imaging
128 workup
129 schema:name Assessment of carotid atherosclerotic disease using three-dimensional cardiovascular magnetic resonance vessel wall imaging: comparison with digital subtraction angiography
130 schema:pagination 18
131 schema:productId N581d422a9c284e9593a2e80400c17a47
132 N9b638ce7036649478fa697db7b638f9e
133 Nf9ab2365abe747bd8ae798525d3e33ca
134 schema:sameAs https://app.dimensions.ai/details/publication/pub.1125401947
135 https://doi.org/10.1186/s12968-020-0604-x
136 schema:sdDatePublished 2022-12-01T06:41
137 schema:sdLicense https://scigraph.springernature.com/explorer/license/
138 schema:sdPublisher N184add47ed2c450fbbf54519f1ff3289
139 schema:url https://doi.org/10.1186/s12968-020-0604-x
140 sgo:license sg:explorer/license/
141 sgo:sdDataset articles
142 rdf:type schema:ScholarlyArticle
143 N076391da7f6a4f1bbb2d2a587cecdd21 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
144 schema:name Humans
145 rdf:type schema:DefinedTerm
146 N0be9f1c673ac4b349e211292e105f58c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
147 schema:name Angiography, Digital Subtraction
148 rdf:type schema:DefinedTerm
149 N12b3b0ddb3d843119dd98f769eacf07f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
150 schema:name Severity of Illness Index
151 rdf:type schema:DefinedTerm
152 N15958acc541b492e953490c27eed731e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
153 schema:name Female
154 rdf:type schema:DefinedTerm
155 N184add47ed2c450fbbf54519f1ff3289 schema:name Springer Nature - SN SciGraph project
156 rdf:type schema:Organization
157 N1904af8f53af4b6d92163a0af1949c8f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
158 schema:name Reproducibility of Results
159 rdf:type schema:DefinedTerm
160 N19f82d0ee9f0485ab272e6d24510ccf3 rdf:first sg:person.01136172260.58
161 rdf:rest rdf:nil
162 N2135f9b50e764a8a84aff20d1f9d8cb5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
163 schema:name Magnetic Resonance Angiography
164 rdf:type schema:DefinedTerm
165 N3582fbcf465444d7bddebd81802775d2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
166 schema:name Aged
167 rdf:type schema:DefinedTerm
168 N3948c822faaf45d187f0f52bd664fd41 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
169 schema:name Prospective Studies
170 rdf:type schema:DefinedTerm
171 N405d8b5ca6b1496bba97da1e2e54ee24 rdf:first sg:person.013205052263.44
172 rdf:rest Ndf66dc23953e452e9853bba091d3ddec
173 N487a9c24250e43f292b70d2dd6098746 schema:volumeNumber 22
174 rdf:type schema:PublicationVolume
175 N4afa24912ac640ad95212ff4b5d44352 rdf:first sg:person.01143631075.45
176 rdf:rest N19f82d0ee9f0485ab272e6d24510ccf3
177 N4db4c34046194f3cb4d9dc627bf8614e rdf:first sg:person.011173575413.11
178 rdf:rest Nab4e160d6d5b4fa5a94631386e56167a
179 N52498afbd7954e1086101d2889532e2a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
180 schema:name Plaque, Atherosclerotic
181 rdf:type schema:DefinedTerm
182 N537833375b3c43cd889138f855f1e962 schema:issueNumber 1
183 rdf:type schema:PublicationIssue
184 N581d422a9c284e9593a2e80400c17a47 schema:name doi
185 schema:value 10.1186/s12968-020-0604-x
186 rdf:type schema:PropertyValue
187 N58f2d5b7bae84ada968017869bfa8a3c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
188 schema:name Middle Aged
189 rdf:type schema:DefinedTerm
190 N8133155be04a496eb7e0803f21fa1efe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
191 schema:name Imaging, Three-Dimensional
192 rdf:type schema:DefinedTerm
193 N8dd2a586045e4e3a88ca545358568f63 rdf:first sg:person.013371766555.49
194 rdf:rest N4db4c34046194f3cb4d9dc627bf8614e
195 N8f6912734d6348a38a9ed64f8ca7078b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
196 schema:name Predictive Value of Tests
197 rdf:type schema:DefinedTerm
198 N9b638ce7036649478fa697db7b638f9e schema:name dimensions_id
199 schema:value pub.1125401947
200 rdf:type schema:PropertyValue
201 Nab4e160d6d5b4fa5a94631386e56167a rdf:first sg:person.012016701452.48
202 rdf:rest N405d8b5ca6b1496bba97da1e2e54ee24
203 Nd31493017138465ea6b47b26100c0a92 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
204 schema:name Carotid Stenosis
205 rdf:type schema:DefinedTerm
206 Ndf66dc23953e452e9853bba091d3ddec rdf:first sg:person.01152021525.33
207 rdf:rest N4afa24912ac640ad95212ff4b5d44352
208 Ne7977103ba3a4a08b053a2898205c77b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
209 schema:name Male
210 rdf:type schema:DefinedTerm
211 Nf9ab2365abe747bd8ae798525d3e33ca schema:name pubmed_id
212 schema:value 32131854
213 rdf:type schema:PropertyValue
214 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
215 schema:name Medical and Health Sciences
216 rdf:type schema:DefinedTerm
217 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
218 schema:name Cardiorespiratory Medicine and Haematology
219 rdf:type schema:DefinedTerm
220 sg:grant.8388914 http://pending.schema.org/fundedItem sg:pub.10.1186/s12968-020-0604-x
221 rdf:type schema:MonetaryGrant
222 sg:journal.1030439 schema:issn 1548-7679
223 1879-2855
224 schema:name Journal of Cardiovascular Magnetic Resonance
225 schema:publisher Springer Nature
226 rdf:type schema:Periodical
227 sg:person.011173575413.11 schema:affiliation grid-institutes:grid.24696.3f
228 schema:familyName Lu
229 schema:givenName Mi
230 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011173575413.11
231 rdf:type schema:Person
232 sg:person.01136172260.58 schema:affiliation grid-institutes:grid.19006.3e
233 schema:familyName Fan
234 schema:givenName Zhaoyang
235 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136172260.58
236 rdf:type schema:Person
237 sg:person.01143631075.45 schema:affiliation grid-institutes:grid.24696.3f
238 schema:familyName Yu
239 schema:givenName Wei
240 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01143631075.45
241 rdf:type schema:Person
242 sg:person.01152021525.33 schema:affiliation grid-institutes:grid.19006.3e
243 schema:familyName Li
244 schema:givenName Debiao
245 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152021525.33
246 rdf:type schema:Person
247 sg:person.012016701452.48 schema:affiliation grid-institutes:grid.24696.3f
248 schema:familyName Liu
249 schema:givenName Wen
250 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012016701452.48
251 rdf:type schema:Person
252 sg:person.013205052263.44 schema:affiliation grid-institutes:grid.24696.3f
253 schema:familyName Zheng
254 schema:givenName Tiejin
255 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013205052263.44
256 rdf:type schema:Person
257 sg:person.013371766555.49 schema:affiliation grid-institutes:grid.24696.3f
258 schema:familyName Wang
259 schema:givenName Zhenjia
260 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013371766555.49
261 rdf:type schema:Person
262 sg:pub.10.1007/s10554-017-1228-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091201460
263 https://doi.org/10.1007/s10554-017-1228-6
264 rdf:type schema:CreativeWork
265 sg:pub.10.1038/srep33246 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008183501
266 https://doi.org/10.1038/srep33246
267 rdf:type schema:CreativeWork
268 sg:pub.10.1186/1532-429x-10-31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042265023
269 https://doi.org/10.1186/1532-429x-10-31
270 rdf:type schema:CreativeWork
271 sg:pub.10.1186/1532-429x-11-41 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003933091
272 https://doi.org/10.1186/1532-429x-11-41
273 rdf:type schema:CreativeWork
274 sg:pub.10.1186/s12968-018-0453-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1104560508
275 https://doi.org/10.1186/s12968-018-0453-z
276 rdf:type schema:CreativeWork
277 grid-institutes:grid.19006.3e schema:alternateName Department of Bioengineering, University of California, Los Angeles, CA, USA
278 schema:name Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., PACT 400, 90048, Los Angeles, CA, USA
279 Department of Bioengineering, University of California, Los Angeles, CA, USA
280 rdf:type schema:Organization
281 grid-institutes:grid.24696.3f schema:alternateName Department of Neurosurgery, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, 100029, Beijing, China
282 Department of Otolaryngology Head and Neck Surgery, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, 100029, Beijing, China
283 Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2 Anzhen Road, 100029, Beijing, China
284 Department of Radiology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, No. 23, Back Road of Art Gallery, 100010, Beijing, China
285 schema:name Department of Neurosurgery, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, 100029, Beijing, China
286 Department of Otolaryngology Head and Neck Surgery, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, 100029, Beijing, China
287 Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2 Anzhen Road, 100029, Beijing, China
288 Department of Radiology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, No. 23, Back Road of Art Gallery, 100010, Beijing, China
289 rdf:type schema:Organization
 




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


...