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.24696.3f", 
          "name": [
            "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yu", 
        "givenName": "Wei", 
        "id": "sg:person.01143631075.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01143631075.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Bioengineering, University of California, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.266100.3", 
          "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.24696.3f", 
          "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.24696.3f", 
          "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.24696.3f", 
          "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.24696.3f", 
          "name": [
            "Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Zhaoqi", 
        "id": "sg:person.014030237157.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014030237157.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Bioengineering, University of California, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.266100.3", 
          "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": "2022-10-01T06:40", 
    "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_636.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 N0211ff00f87a4443b8b5732c151226a9
2 N067b942dbfa743d1b286332087b0c921
3 N2929b134a90842c0bdc80fee60c94882
4 N29f0db32e8924e49afb17ee205772758
5 N328a3807c96e414cb31a45dc52b03368
6 N410741cc76f54c938337ae2c884211cf
7 N455dbdade01142e9bb17a4ed4d717760
8 N4893a3928cb64deba27da027e5c12663
9 N72fba832d0434db4b471016f2dce2494
10 N7a4124dffa5b4d698bb85c934b15613f
11 N7bc51e56f70544f0be4a666f726bcf1f
12 N8c1ee3ef58fe4c7ebdda4f1befba0bd7
13 N8d8924e293964d98b606aa96fa6c6f3f
14 N95ad98abc9494cb2b58510b5495f502b
15 N9d78bb61699844449cfbb68b6c08611a
16 Na37883d205334adeb1207608d3e492ab
17 Nc20e7b28543447d2a509004cb8ad66b8
18 Nccdf92dff117449b9842e54891f70c48
19 Nd8eb1402cd4e4d5a8bdcbd119cd07c4c
20 Neaf04e853219457f9add62648f398bea
21 Nee293cae9aeb4477a1bec383c91ad501
22 Nef82f3fe2c944cc498bc24d192e1be1b
23 Nf9499b9de8d4428aaf6dd308fe86741e
24 anzsrc-for:11
25 anzsrc-for:1102
26 schema:author N0d764b1556ad4f4681486d2d33b2ff7a
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 Na5d058ee6d934d9ab9a66a8334277ea5
33 Nd231eb936d3342bd8a2175d7be1e1313
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 N56960fcb2e1c4f0bb5cf34b82824646d
141 N9952d973fd1b408a9c7faf29b344cd90
142 Nacffcace71c645f79a99fceedf374b0c
143 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023348486
144 https://doi.org/10.1186/s12968-014-0053-5
145 schema:sdDatePublished 2022-10-01T06:40
146 schema:sdLicense https://scigraph.springernature.com/explorer/license/
147 schema:sdPublisher N3bc8d4fbdfcd41d3ab02059c8caf0937
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 N0211ff00f87a4443b8b5732c151226a9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
153 schema:name Carotid Stenosis
154 rdf:type schema:DefinedTerm
155 N067b942dbfa743d1b286332087b0c921 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
156 schema:name Vascular Calcification
157 rdf:type schema:DefinedTerm
158 N0d764b1556ad4f4681486d2d33b2ff7a rdf:first sg:person.01136172260.58
159 rdf:rest N76c848c5ce634ce3aad70dc6d9a357e5
160 N161bbddf553744449e53ecd27f4c358d rdf:first sg:person.0636352402.71
161 rdf:rest Nd8234856b3404a228b2816a0fe82bd1b
162 N2929b134a90842c0bdc80fee60c94882 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
163 schema:name Middle Aged
164 rdf:type schema:DefinedTerm
165 N29f0db32e8924e49afb17ee205772758 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
166 schema:name Necrosis
167 rdf:type schema:DefinedTerm
168 N328a3807c96e414cb31a45dc52b03368 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
169 schema:name Magnetic Resonance Angiography
170 rdf:type schema:DefinedTerm
171 N3bc8d4fbdfcd41d3ab02059c8caf0937 schema:name Springer Nature - SN SciGraph project
172 rdf:type schema:Organization
173 N3cbc6cfcf7ce4ab986b18f39ae1cc8d2 rdf:first sg:person.0711400762.27
174 rdf:rest N161bbddf553744449e53ecd27f4c358d
175 N410741cc76f54c938337ae2c884211cf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
176 schema:name Reproducibility of Results
177 rdf:type schema:DefinedTerm
178 N455dbdade01142e9bb17a4ed4d717760 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
179 schema:name Aged
180 rdf:type schema:DefinedTerm
181 N473cc385205a4809b36936992514215c rdf:first sg:person.01161036206.83
182 rdf:rest Ndc2bd827ebc94e8e872f2571d87a9210
183 N4893a3928cb64deba27da027e5c12663 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
184 schema:name Pilot Projects
185 rdf:type schema:DefinedTerm
186 N56960fcb2e1c4f0bb5cf34b82824646d schema:name dimensions_id
187 schema:value pub.1023348486
188 rdf:type schema:PropertyValue
189 N608f8c414d7e46c29f9546b801111f66 rdf:first sg:person.01320556462.83
190 rdf:rest N473cc385205a4809b36936992514215c
191 N6c31cc74af244b08bef2ed423b9dddb4 rdf:first sg:person.0603101342.45
192 rdf:rest N71849e1bc7a84287ba7ff8879a4456bd
193 N71849e1bc7a84287ba7ff8879a4456bd rdf:first sg:person.01161155735.08
194 rdf:rest Nb340603bec254ddf8536b72a5a4f1550
195 N72fba832d0434db4b471016f2dce2494 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
196 schema:name Computer Simulation
197 rdf:type schema:DefinedTerm
198 N76c848c5ce634ce3aad70dc6d9a357e5 rdf:first sg:person.01143631075.45
199 rdf:rest N608f8c414d7e46c29f9546b801111f66
200 N7a4124dffa5b4d698bb85c934b15613f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
201 schema:name Fibrosis
202 rdf:type schema:DefinedTerm
203 N7bc51e56f70544f0be4a666f726bcf1f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
204 schema:name Plaque, Atherosclerotic
205 rdf:type schema:DefinedTerm
206 N8c1ee3ef58fe4c7ebdda4f1befba0bd7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
207 schema:name Signal-To-Noise Ratio
208 rdf:type schema:DefinedTerm
209 N8d8924e293964d98b606aa96fa6c6f3f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
210 schema:name Predictive Value of Tests
211 rdf:type schema:DefinedTerm
212 N95ad98abc9494cb2b58510b5495f502b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
213 schema:name Carotid Arteries
214 rdf:type schema:DefinedTerm
215 N95d92583201b46b7841b770cd4970a6c rdf:first sg:person.01152021525.33
216 rdf:rest rdf:nil
217 N9952d973fd1b408a9c7faf29b344cd90 schema:name doi
218 schema:value 10.1186/s12968-014-0053-5
219 rdf:type schema:PropertyValue
220 N9d78bb61699844449cfbb68b6c08611a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
221 schema:name Male
222 rdf:type schema:DefinedTerm
223 Na23415e235a14d7ea65aaa2f9be7c47e rdf:first sg:person.0620532177.31
224 rdf:rest N6c31cc74af244b08bef2ed423b9dddb4
225 Na37883d205334adeb1207608d3e492ab schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
226 schema:name Hemorrhage
227 rdf:type schema:DefinedTerm
228 Na45ec293ec8646f3843729a8fc34f401 rdf:first sg:person.01066175422.29
229 rdf:rest Na23415e235a14d7ea65aaa2f9be7c47e
230 Na5d058ee6d934d9ab9a66a8334277ea5 schema:issueNumber 1
231 rdf:type schema:PublicationIssue
232 Nacffcace71c645f79a99fceedf374b0c schema:name pubmed_id
233 schema:value 25184808
234 rdf:type schema:PropertyValue
235 Nb340603bec254ddf8536b72a5a4f1550 rdf:first sg:person.014030237157.28
236 rdf:rest N95d92583201b46b7841b770cd4970a6c
237 Nc20e7b28543447d2a509004cb8ad66b8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
238 schema:name Algorithms
239 rdf:type schema:DefinedTerm
240 Nccdf92dff117449b9842e54891f70c48 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
241 schema:name Contrast Media
242 rdf:type schema:DefinedTerm
243 Nd231eb936d3342bd8a2175d7be1e1313 schema:volumeNumber 16
244 rdf:type schema:PublicationVolume
245 Nd8234856b3404a228b2816a0fe82bd1b rdf:first sg:person.01066244563.44
246 rdf:rest Na45ec293ec8646f3843729a8fc34f401
247 Nd8eb1402cd4e4d5a8bdcbd119cd07c4c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
248 schema:name Feasibility Studies
249 rdf:type schema:DefinedTerm
250 Ndc2bd827ebc94e8e872f2571d87a9210 rdf:first sg:person.0643265562.19
251 rdf:rest N3cbc6cfcf7ce4ab986b18f39ae1cc8d2
252 Neaf04e853219457f9add62648f398bea schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
253 schema:name Image Interpretation, Computer-Assisted
254 rdf:type schema:DefinedTerm
255 Nee293cae9aeb4477a1bec383c91ad501 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
256 schema:name Imaging, Three-Dimensional
257 rdf:type schema:DefinedTerm
258 Nef82f3fe2c944cc498bc24d192e1be1b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
259 schema:name Models, Cardiovascular
260 rdf:type schema:DefinedTerm
261 Nf9499b9de8d4428aaf6dd308fe86741e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
262 schema:name Humans
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.24696.3f
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.266100.3
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.24696.3f
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.266100.3
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.24696.3f
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.24696.3f
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.24696.3f
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.24696.3f schema:alternateName Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China
351 schema:name Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China
352 rdf:type schema:Organization
353 grid-institutes:grid.266100.3 schema:alternateName Department of Bioengineering, University of California, Los Angeles, CA, USA
354 schema:name Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
355 Department of Bioengineering, University of California, Los Angeles, CA, USA
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)


...