Multi-contrast atherosclerosis characterization (MATCH) of carotid plaque with a single 5-min scan: technical development and clinical feasibility View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-07-25

AUTHORS

Zhaoyang Fan, Wei Yu, Yibin Xie, Li Dong, Lixin Yang, Zhanhong Wang, Antonio Hernandez Conte, Xiaoming Bi, Jing An, Tianjing Zhang, Gerhard Laub, Prediman Krishan Shah, Zhaoqi Zhang, Debiao Li

ABSTRACT

BackgroundMulti-contrast weighted imaging is a commonly used cardiovascular magnetic resonance (CMR) protocol for characterization of carotid plaque composition. However, this approach is limited in several aspects including low slice resolution, long scan time, image mis-registration, and complex image interpretation. In this work, a 3D CMR technique, named Multi-contrast Atherosclerosis Characterization (MATCH), was developed to mitigate the above limitations.MethodsMATCH employs a 3D spoiled segmented fast low angle shot readout to acquire data with three different contrast weightings in an interleaved fashion. The inherently co-registered image sets, hyper T1-weighting, gray blood, and T2-weighting, are used to detect intra-plaque hemorrhage (IPH), calcification (CA), lipid-rich necrotic core (LRNC), and loose-matrix (LM). The MATCH sequence was optimized by computer simulations and testing on four healthy volunteers and then evaluated in a pilot study of six patients with carotid plaque, using the conventional multi-contrast protocol as a reference.ResultsOn MATCH images, the major plaque components were easy to identify. Spatial co-registration between the three image sets with MATCH was particularly helpful for the reviewer to discern co-existent components in an image and appreciate their spatial relation. Based on Cohen’s kappa tests, moderate to excellent agreement in the image-based or artery-based component detection between the two protocols was obtained for LRNC, IPH, CA, and LM, respectively. Compared with the conventional multi-contrast protocol, the MATCH protocol yield significantly higher signal contrast ratio for IPH (3.1 ± 1.3 vs. 0.4 ± 0.3, p < 0.001) and CA (1.6 ± 1.5 vs. 0.7 ± 0.6, p = 0.012) with respect to the vessel wall.ConclusionsTo the best of our knowledge, the proposed MATCH sequence is the first 3D CMR technique that acquires spatially co-registered multi-contrast image sets in a single scan for characterization of carotid plaque composition. Our pilot clinical study suggests that the MATCH-based protocol may outperform the conventional multi-contrast protocol in several respects. With further technical improvements and large-scale clinical validation, MATCH has the potential to become a CMR method for assessing the risk of plaque disruption in a clinical workup. More... »

PAGES

53

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12968-014-0053-5

DOI

http://dx.doi.org/10.1186/s12968-014-0053-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023348486

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25184808


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": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Carotid Arteries", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Carotid Stenosis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computer Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Contrast Media", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Feasibility Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fibrosis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Hemorrhage", 
        "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": "Models, Cardiovascular", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Necrosis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Pilot Projects", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Plaque, Atherosclerotic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Predictive Value of Tests", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Signal-To-Noise Ratio", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Vascular Calcification", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.50956.3f", 
          "name": [
            "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fan", 
        "givenName": "Zhaoyang", 
        "id": "sg:person.01136172260.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136172260.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.411606.4", 
          "name": [
            "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yu", 
        "givenName": "Wei", 
        "id": "sg:person.01143631075.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01143631075.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Bioengineering, University of California, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.19006.3e", 
          "name": [
            "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA", 
            "Department of Bioengineering, University of California, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xie", 
        "givenName": "Yibin", 
        "id": "sg:person.01320556462.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01320556462.83"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.411606.4", 
          "name": [
            "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dong", 
        "givenName": "Li", 
        "id": "sg:person.01161036206.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01161036206.83"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.411606.4", 
          "name": [
            "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yang", 
        "givenName": "Lixin", 
        "id": "sg:person.0643265562.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0643265562.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.411606.4", 
          "name": [
            "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Zhanhong", 
        "id": "sg:person.0711400762.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0711400762.27"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Anesthesiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.50956.3f", 
          "name": [
            "Department of Anesthesiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Conte", 
        "givenName": "Antonio Hernandez", 
        "id": "sg:person.0636352402.71", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636352402.71"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MR R&D, Siemens Healthcare, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.415886.6", 
          "name": [
            "MR R&D, Siemens Healthcare, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bi", 
        "givenName": "Xiaoming", 
        "id": "sg:person.01066244563.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066244563.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MR Collaborations NE Asia, Siemens Healthcare, Beijing, China", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "MR Collaborations NE Asia, Siemens Healthcare, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "An", 
        "givenName": "Jing", 
        "id": "sg:person.01066175422.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066175422.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MR Collaborations NE Asia, Siemens Healthcare, Beijing, China", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "MR Collaborations NE Asia, Siemens Healthcare, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Tianjing", 
        "id": "sg:person.0620532177.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0620532177.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MR R&D, Siemens Healthcare, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.415886.6", 
          "name": [
            "MR R&D, Siemens Healthcare, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Laub", 
        "givenName": "Gerhard", 
        "id": "sg:person.0603101342.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0603101342.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Atherosclerosis Prevention and Management Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.50956.3f", 
          "name": [
            "Oppenheimer Atherosclerosis Research Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA", 
            "Atherosclerosis Prevention and Management Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shah", 
        "givenName": "Prediman Krishan", 
        "id": "sg:person.01161155735.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01161155735.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China", 
          "id": "http://www.grid.ac/institutes/grid.411606.4", 
          "name": [
            "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Zhaoqi", 
        "id": "sg:person.014030237157.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014030237157.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Bioengineering, University of California, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.19006.3e", 
          "name": [
            "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA", 
            "Department of Bioengineering, University of California, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Debiao", 
        "id": "sg:person.01152021525.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152021525.33"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2014-07-25", 
    "datePublishedReg": "2014-07-25", 
    "description": "BackgroundMulti-contrast weighted imaging is a commonly used cardiovascular magnetic resonance (CMR) protocol for characterization of carotid plaque composition. However, this approach is limited in several aspects including low slice resolution, long scan time, image mis-registration, and complex image interpretation. In this work, a 3D CMR technique, named Multi-contrast Atherosclerosis Characterization (MATCH), was developed to mitigate the above limitations.MethodsMATCH employs a 3D spoiled segmented fast low angle shot readout to acquire data with three different contrast weightings in an interleaved fashion. The inherently co-registered image sets, hyper T1-weighting, gray blood, and T2-weighting, are used to detect intra-plaque hemorrhage (IPH), calcification (CA), lipid-rich necrotic core (LRNC), and loose-matrix (LM). The MATCH sequence was optimized by computer simulations and testing on four healthy volunteers and then evaluated in a pilot study of six patients with carotid plaque, using the conventional multi-contrast protocol as a reference.ResultsOn MATCH images, the major plaque components were easy to identify. Spatial co-registration between the three image sets with MATCH was particularly helpful for the reviewer to discern co-existent components in an image and appreciate their spatial relation. Based on Cohen\u2019s kappa tests, moderate to excellent agreement in the image-based or artery-based component detection between the two protocols was obtained for LRNC, IPH, CA, and LM, respectively. Compared with the conventional multi-contrast protocol, the MATCH protocol yield significantly higher signal contrast ratio for IPH (3.1\u2009\u00b1\u20091.3 vs. 0.4\u2009\u00b1\u20090.3, p\u2009<\u20090.001) and CA (1.6\u2009\u00b1\u20091.5 vs. 0.7\u2009\u00b1\u20090.6, p\u2009=\u20090.012) with respect to the vessel wall.ConclusionsTo the best of our knowledge, the proposed MATCH sequence is the first 3D CMR technique that acquires spatially co-registered multi-contrast image sets in a single scan for characterization of carotid plaque composition. Our pilot clinical study suggests that the MATCH-based protocol may outperform the conventional multi-contrast protocol in several respects. With further technical improvements and large-scale clinical validation, MATCH has the potential to become a CMR method for assessing the risk of plaque disruption in a clinical workup.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s12968-014-0053-5", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2542522", 
        "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": "16"
      }
    ], 
    "keywords": [
      "lipid-rich necrotic core", 
      "intra-plaque hemorrhage", 
      "carotid plaque composition", 
      "carotid plaques", 
      "plaque composition", 
      "CMR techniques", 
      "Kappa test", 
      "cardiovascular magnetic resonance protocol", 
      "atherosclerosis characterization", 
      "pilot clinical study", 
      "large-scale clinical validation", 
      "magnetic resonance protocol", 
      "major plaque components", 
      "Cohen's kappa test", 
      "clinical workup", 
      "plaque disruption", 
      "clinical studies", 
      "fast low angle", 
      "healthy volunteers", 
      "clinical feasibility", 
      "necrotic core", 
      "further technical improvements", 
      "plaque components", 
      "clinical validation", 
      "pilot study", 
      "calcification", 
      "different contrast weightings", 
      "vessel wall", 
      "plaques", 
      "signal contrast ratio", 
      "CMR methods", 
      "contrast weightings", 
      "scans", 
      "hemorrhage", 
      "patients", 
      "image sets", 
      "workup", 
      "protocol", 
      "long scan times", 
      "technical improvements", 
      "blood", 
      "volunteers", 
      "scan time", 
      "reviewers", 
      "risk", 
      "study", 
      "imaging", 
      "ConclusionsTo", 
      "hyper", 
      "image interpretation", 
      "match image", 
      "disruption", 
      "component detection", 
      "above limitations", 
      "interleaved fashion", 
      "test", 
      "spatial relations", 
      "improvement", 
      "fashion", 
      "images", 
      "single scan", 
      "slice resolution", 
      "match sequence", 
      "protocol yields", 
      "technical developments", 
      "data", 
      "wall", 
      "development", 
      "computer simulations", 
      "detection", 
      "feasibility", 
      "components", 
      "set", 
      "knowledge", 
      "ratio", 
      "time", 
      "technique", 
      "potential", 
      "LM", 
      "validation", 
      "characterization", 
      "limitations", 
      "respect", 
      "aspects", 
      "sequence", 
      "relation", 
      "reference", 
      "method", 
      "weighting", 
      "approach", 
      "contrast ratio", 
      "composition", 
      "interpretation", 
      "readout", 
      "resolution", 
      "simulations", 
      "work", 
      "angle", 
      "core", 
      "agreement", 
      "low angle", 
      "yield", 
      "excellent agreement"
    ], 
    "name": "Multi-contrast atherosclerosis characterization (MATCH) of carotid plaque with a single 5-min scan: technical development and clinical feasibility", 
    "pagination": "53", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1023348486"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12968-014-0053-5"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "25184808"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12968-014-0053-5", 
      "https://app.dimensions.ai/details/publication/pub.1023348486"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-12-01T06:32", 
    "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_641.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s12968-014-0053-5"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12968-014-0053-5'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12968-014-0053-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12968-014-0053-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12968-014-0053-5'


 

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

367 TRIPLES      20 PREDICATES      151 URIs      143 LITERALS      30 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12968-014-0053-5 schema:about N134c5d7d38604d1394ce3adbad09912b
2 N1955ad0003c7446aab5154c2cef95c3f
3 N1a0afbf45c8247a78ec7da661533ea5b
4 N2ed07cfd3e1c4dc3800aefc89a0fe972
5 N2f84a498daec4a3f9e8311fcacf3cf0a
6 N30fc946178d24d9781bfe7ee9d631356
7 N312a1c3a37864d2d8ab526737af04d24
8 N43045134399143b5bfed43d3a6507ac8
9 N4f157f6c02d74b9fa01a21393f8c8252
10 N57be491656de4a9bac635342fb375991
11 N644683b73e04495694be309cbada944f
12 N7b39707925f74c199929592fc2b441c8
13 N813ddcce84164133aa58c52b74bfd06f
14 N8deb9602123f42f3baeb690f230c94ed
15 N8fb95b174f44497eaeceb6a49ea91349
16 N9f93f7a30ece4cb68903131730442292
17 Nb290467c3cdf4d0dbb650e6781986348
18 Ncfce5e65d2de48ae830599eb8bf9909b
19 Neb6e2f3295ef4378a1f45789152ae3d6
20 Nef2fdc5886d84840a34b951d33be1c9a
21 Nf1327eb0b17e4c75b2dd2044c142ed54
22 Nf7996f742e0f4ddfadcee3bcedbbaa4b
23 Nf951674dddff44a4b94c80d0868eb43b
24 anzsrc-for:11
25 anzsrc-for:1102
26 schema:author Ne9e25d96a9334da28b5af38331045a53
27 schema:datePublished 2014-07-25
28 schema:datePublishedReg 2014-07-25
29 schema:description BackgroundMulti-contrast weighted imaging is a commonly used cardiovascular magnetic resonance (CMR) protocol for characterization of carotid plaque composition. However, this approach is limited in several aspects including low slice resolution, long scan time, image mis-registration, and complex image interpretation. In this work, a 3D CMR technique, named Multi-contrast Atherosclerosis Characterization (MATCH), was developed to mitigate the above limitations.MethodsMATCH employs a 3D spoiled segmented fast low angle shot readout to acquire data with three different contrast weightings in an interleaved fashion. The inherently co-registered image sets, hyper T1-weighting, gray blood, and T2-weighting, are used to detect intra-plaque hemorrhage (IPH), calcification (CA), lipid-rich necrotic core (LRNC), and loose-matrix (LM). The MATCH sequence was optimized by computer simulations and testing on four healthy volunteers and then evaluated in a pilot study of six patients with carotid plaque, using the conventional multi-contrast protocol as a reference.ResultsOn MATCH images, the major plaque components were easy to identify. Spatial co-registration between the three image sets with MATCH was particularly helpful for the reviewer to discern co-existent components in an image and appreciate their spatial relation. Based on Cohen’s kappa tests, moderate to excellent agreement in the image-based or artery-based component detection between the two protocols was obtained for LRNC, IPH, CA, and LM, respectively. Compared with the conventional multi-contrast protocol, the MATCH protocol yield significantly higher signal contrast ratio for IPH (3.1 ± 1.3 vs. 0.4 ± 0.3, p < 0.001) and CA (1.6 ± 1.5 vs. 0.7 ± 0.6, p = 0.012) with respect to the vessel wall.ConclusionsTo the best of our knowledge, the proposed MATCH sequence is the first 3D CMR technique that acquires spatially co-registered multi-contrast image sets in a single scan for characterization of carotid plaque composition. Our pilot clinical study suggests that the MATCH-based protocol may outperform the conventional multi-contrast protocol in several respects. With further technical improvements and large-scale clinical validation, MATCH has the potential to become a CMR method for assessing the risk of plaque disruption in a clinical workup.
30 schema:genre article
31 schema:isAccessibleForFree true
32 schema:isPartOf Na053d3c3e9c841bc9901f97997c21af3
33 Na868016319334b45bda0aaeb5c832c85
34 sg:journal.1030439
35 schema:keywords CMR methods
36 CMR techniques
37 Cohen's kappa test
38 ConclusionsTo
39 Kappa test
40 LM
41 above limitations
42 agreement
43 angle
44 approach
45 aspects
46 atherosclerosis characterization
47 blood
48 calcification
49 cardiovascular magnetic resonance protocol
50 carotid plaque composition
51 carotid plaques
52 characterization
53 clinical feasibility
54 clinical studies
55 clinical validation
56 clinical workup
57 component detection
58 components
59 composition
60 computer simulations
61 contrast ratio
62 contrast weightings
63 core
64 data
65 detection
66 development
67 different contrast weightings
68 disruption
69 excellent agreement
70 fashion
71 fast low angle
72 feasibility
73 further technical improvements
74 healthy volunteers
75 hemorrhage
76 hyper
77 image interpretation
78 image sets
79 images
80 imaging
81 improvement
82 interleaved fashion
83 interpretation
84 intra-plaque hemorrhage
85 knowledge
86 large-scale clinical validation
87 limitations
88 lipid-rich necrotic core
89 long scan times
90 low angle
91 magnetic resonance protocol
92 major plaque components
93 match image
94 match sequence
95 method
96 necrotic core
97 patients
98 pilot clinical study
99 pilot study
100 plaque components
101 plaque composition
102 plaque disruption
103 plaques
104 potential
105 protocol
106 protocol yields
107 ratio
108 readout
109 reference
110 relation
111 resolution
112 respect
113 reviewers
114 risk
115 scan time
116 scans
117 sequence
118 set
119 signal contrast ratio
120 simulations
121 single scan
122 slice resolution
123 spatial relations
124 study
125 technical developments
126 technical improvements
127 technique
128 test
129 time
130 validation
131 vessel wall
132 volunteers
133 wall
134 weighting
135 work
136 workup
137 yield
138 schema:name Multi-contrast atherosclerosis characterization (MATCH) of carotid plaque with a single 5-min scan: technical development and clinical feasibility
139 schema:pagination 53
140 schema:productId N4087ecb5dae440f995214c1ad1ceea0c
141 Ndbbea3ed44fc4d4ca4bad8c02e63057f
142 Ndbe9a95c75b44ab9a0e13409f13a9b81
143 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023348486
144 https://doi.org/10.1186/s12968-014-0053-5
145 schema:sdDatePublished 2022-12-01T06:32
146 schema:sdLicense https://scigraph.springernature.com/explorer/license/
147 schema:sdPublisher N917bb46a69b644738c178fe2cf2cc1f1
148 schema:url https://doi.org/10.1186/s12968-014-0053-5
149 sgo:license sg:explorer/license/
150 sgo:sdDataset articles
151 rdf:type schema:ScholarlyArticle
152 N05aefeb035d34f7cae89979fe1974805 rdf:first sg:person.01143631075.45
153 rdf:rest Nc4de176d5c8b40a1b209946addd40e2d
154 N0f5bdc997ada40079b388b3ef1c8cf94 rdf:first sg:person.0603101342.45
155 rdf:rest N2a42140b41c44697b8a0203cc70fbb38
156 N12b87f4730d34a04b3d7a192264b5824 rdf:first sg:person.01161036206.83
157 rdf:rest N1a432691723c41cfa114feb709d5ffbe
158 N134c5d7d38604d1394ce3adbad09912b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
159 schema:name Necrosis
160 rdf:type schema:DefinedTerm
161 N1955ad0003c7446aab5154c2cef95c3f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Carotid Stenosis
163 rdf:type schema:DefinedTerm
164 N1a0afbf45c8247a78ec7da661533ea5b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
165 schema:name Humans
166 rdf:type schema:DefinedTerm
167 N1a432691723c41cfa114feb709d5ffbe rdf:first sg:person.0643265562.19
168 rdf:rest N6cc28095b5e649d5a4ca6c32c94af95f
169 N2a42140b41c44697b8a0203cc70fbb38 rdf:first sg:person.01161155735.08
170 rdf:rest N700c8d321401477db95cdf4d414bd013
171 N2ed07cfd3e1c4dc3800aefc89a0fe972 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
172 schema:name Fibrosis
173 rdf:type schema:DefinedTerm
174 N2f84a498daec4a3f9e8311fcacf3cf0a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
175 schema:name Feasibility Studies
176 rdf:type schema:DefinedTerm
177 N30fc946178d24d9781bfe7ee9d631356 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
178 schema:name Reproducibility of Results
179 rdf:type schema:DefinedTerm
180 N312a1c3a37864d2d8ab526737af04d24 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
181 schema:name Middle Aged
182 rdf:type schema:DefinedTerm
183 N3193b48d1fb94f71a1ac113552cf1848 rdf:first sg:person.0636352402.71
184 rdf:rest N4989244258124fd6b0f7065b61ee7b36
185 N3d432a6ad75f47dda8d9ae5c35583acd rdf:first sg:person.01066175422.29
186 rdf:rest N6a863a6ab3e4450da5e734cb14cbe9bd
187 N4087ecb5dae440f995214c1ad1ceea0c schema:name dimensions_id
188 schema:value pub.1023348486
189 rdf:type schema:PropertyValue
190 N43045134399143b5bfed43d3a6507ac8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
191 schema:name Pilot Projects
192 rdf:type schema:DefinedTerm
193 N4989244258124fd6b0f7065b61ee7b36 rdf:first sg:person.01066244563.44
194 rdf:rest N3d432a6ad75f47dda8d9ae5c35583acd
195 N4f157f6c02d74b9fa01a21393f8c8252 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
196 schema:name Magnetic Resonance Angiography
197 rdf:type schema:DefinedTerm
198 N57be491656de4a9bac635342fb375991 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
199 schema:name Imaging, Three-Dimensional
200 rdf:type schema:DefinedTerm
201 N644683b73e04495694be309cbada944f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
202 schema:name Signal-To-Noise Ratio
203 rdf:type schema:DefinedTerm
204 N6a863a6ab3e4450da5e734cb14cbe9bd rdf:first sg:person.0620532177.31
205 rdf:rest N0f5bdc997ada40079b388b3ef1c8cf94
206 N6cc28095b5e649d5a4ca6c32c94af95f rdf:first sg:person.0711400762.27
207 rdf:rest N3193b48d1fb94f71a1ac113552cf1848
208 N700c8d321401477db95cdf4d414bd013 rdf:first sg:person.014030237157.28
209 rdf:rest Nabac9fbeac6a4f76a9c58d17e6428184
210 N7b39707925f74c199929592fc2b441c8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
211 schema:name Computer Simulation
212 rdf:type schema:DefinedTerm
213 N813ddcce84164133aa58c52b74bfd06f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
214 schema:name Male
215 rdf:type schema:DefinedTerm
216 N8deb9602123f42f3baeb690f230c94ed schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
217 schema:name Algorithms
218 rdf:type schema:DefinedTerm
219 N8fb95b174f44497eaeceb6a49ea91349 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
220 schema:name Predictive Value of Tests
221 rdf:type schema:DefinedTerm
222 N917bb46a69b644738c178fe2cf2cc1f1 schema:name Springer Nature - SN SciGraph project
223 rdf:type schema:Organization
224 N9f93f7a30ece4cb68903131730442292 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
225 schema:name Aged
226 rdf:type schema:DefinedTerm
227 Na053d3c3e9c841bc9901f97997c21af3 schema:issueNumber 1
228 rdf:type schema:PublicationIssue
229 Na868016319334b45bda0aaeb5c832c85 schema:volumeNumber 16
230 rdf:type schema:PublicationVolume
231 Nabac9fbeac6a4f76a9c58d17e6428184 rdf:first sg:person.01152021525.33
232 rdf:rest rdf:nil
233 Nb290467c3cdf4d0dbb650e6781986348 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
234 schema:name Contrast Media
235 rdf:type schema:DefinedTerm
236 Nc4de176d5c8b40a1b209946addd40e2d rdf:first sg:person.01320556462.83
237 rdf:rest N12b87f4730d34a04b3d7a192264b5824
238 Ncfce5e65d2de48ae830599eb8bf9909b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
239 schema:name Models, Cardiovascular
240 rdf:type schema:DefinedTerm
241 Ndbbea3ed44fc4d4ca4bad8c02e63057f schema:name pubmed_id
242 schema:value 25184808
243 rdf:type schema:PropertyValue
244 Ndbe9a95c75b44ab9a0e13409f13a9b81 schema:name doi
245 schema:value 10.1186/s12968-014-0053-5
246 rdf:type schema:PropertyValue
247 Ne9e25d96a9334da28b5af38331045a53 rdf:first sg:person.01136172260.58
248 rdf:rest N05aefeb035d34f7cae89979fe1974805
249 Neb6e2f3295ef4378a1f45789152ae3d6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
250 schema:name Image Interpretation, Computer-Assisted
251 rdf:type schema:DefinedTerm
252 Nef2fdc5886d84840a34b951d33be1c9a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
253 schema:name Plaque, Atherosclerotic
254 rdf:type schema:DefinedTerm
255 Nf1327eb0b17e4c75b2dd2044c142ed54 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
256 schema:name Hemorrhage
257 rdf:type schema:DefinedTerm
258 Nf7996f742e0f4ddfadcee3bcedbbaa4b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
259 schema:name Vascular Calcification
260 rdf:type schema:DefinedTerm
261 Nf951674dddff44a4b94c80d0868eb43b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
262 schema:name Carotid Arteries
263 rdf:type schema:DefinedTerm
264 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
265 schema:name Medical and Health Sciences
266 rdf:type schema:DefinedTerm
267 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
268 schema:name Cardiorespiratory Medicine and Haematology
269 rdf:type schema:DefinedTerm
270 sg:grant.2542522 http://pending.schema.org/fundedItem sg:pub.10.1186/s12968-014-0053-5
271 rdf:type schema:MonetaryGrant
272 sg:journal.1030439 schema:issn 1548-7679
273 1879-2855
274 schema:name Journal of Cardiovascular Magnetic Resonance
275 schema:publisher Springer Nature
276 rdf:type schema:Periodical
277 sg:person.01066175422.29 schema:affiliation grid-institutes:None
278 schema:familyName An
279 schema:givenName Jing
280 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066175422.29
281 rdf:type schema:Person
282 sg:person.01066244563.44 schema:affiliation grid-institutes:grid.415886.6
283 schema:familyName Bi
284 schema:givenName Xiaoming
285 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066244563.44
286 rdf:type schema:Person
287 sg:person.01136172260.58 schema:affiliation grid-institutes:grid.50956.3f
288 schema:familyName Fan
289 schema:givenName Zhaoyang
290 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136172260.58
291 rdf:type schema:Person
292 sg:person.01143631075.45 schema:affiliation grid-institutes:grid.411606.4
293 schema:familyName Yu
294 schema:givenName Wei
295 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01143631075.45
296 rdf:type schema:Person
297 sg:person.01152021525.33 schema:affiliation grid-institutes:grid.19006.3e
298 schema:familyName Li
299 schema:givenName Debiao
300 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152021525.33
301 rdf:type schema:Person
302 sg:person.01161036206.83 schema:affiliation grid-institutes:grid.411606.4
303 schema:familyName Dong
304 schema:givenName Li
305 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01161036206.83
306 rdf:type schema:Person
307 sg:person.01161155735.08 schema:affiliation grid-institutes:grid.50956.3f
308 schema:familyName Shah
309 schema:givenName Prediman Krishan
310 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01161155735.08
311 rdf:type schema:Person
312 sg:person.01320556462.83 schema:affiliation grid-institutes:grid.19006.3e
313 schema:familyName Xie
314 schema:givenName Yibin
315 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01320556462.83
316 rdf:type schema:Person
317 sg:person.014030237157.28 schema:affiliation grid-institutes:grid.411606.4
318 schema:familyName Zhang
319 schema:givenName Zhaoqi
320 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014030237157.28
321 rdf:type schema:Person
322 sg:person.0603101342.45 schema:affiliation grid-institutes:grid.415886.6
323 schema:familyName Laub
324 schema:givenName Gerhard
325 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0603101342.45
326 rdf:type schema:Person
327 sg:person.0620532177.31 schema:affiliation grid-institutes:None
328 schema:familyName Zhang
329 schema:givenName Tianjing
330 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0620532177.31
331 rdf:type schema:Person
332 sg:person.0636352402.71 schema:affiliation grid-institutes:grid.50956.3f
333 schema:familyName Conte
334 schema:givenName Antonio Hernandez
335 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636352402.71
336 rdf:type schema:Person
337 sg:person.0643265562.19 schema:affiliation grid-institutes:grid.411606.4
338 schema:familyName Yang
339 schema:givenName Lixin
340 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0643265562.19
341 rdf:type schema:Person
342 sg:person.0711400762.27 schema:affiliation grid-institutes:grid.411606.4
343 schema:familyName Wang
344 schema:givenName Zhanhong
345 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0711400762.27
346 rdf:type schema:Person
347 grid-institutes:None schema:alternateName MR Collaborations NE Asia, Siemens Healthcare, Beijing, China
348 schema:name MR Collaborations NE Asia, Siemens Healthcare, Beijing, China
349 rdf:type schema:Organization
350 grid-institutes:grid.19006.3e schema:alternateName Department of Bioengineering, University of California, Los Angeles, CA, USA
351 schema:name Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
352 Department of Bioengineering, University of California, Los Angeles, CA, USA
353 rdf:type schema:Organization
354 grid-institutes:grid.411606.4 schema:alternateName Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China
355 schema:name Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China
356 rdf:type schema:Organization
357 grid-institutes:grid.415886.6 schema:alternateName MR R&D, Siemens Healthcare, Los Angeles, CA, USA
358 schema:name MR R&D, Siemens Healthcare, Los Angeles, CA, USA
359 rdf:type schema:Organization
360 grid-institutes:grid.50956.3f schema:alternateName Atherosclerosis Prevention and Management Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
361 Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
362 Department of Anesthesiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
363 schema:name Atherosclerosis Prevention and Management Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
364 Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
365 Department of Anesthesiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
366 Oppenheimer Atherosclerosis Research Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
367 rdf:type schema:Organization
 




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


...