Blooming Artifact Reduction in Coronary Artery Calcification by A New De-blooming Algorithm: Initial Study View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Ping Li, Lei Xu, Lin Yang, Rui Wang, Jiang Hsieh, Zhonghua Sun, Zhanming Fan, Jonathon A. Leipsic

ABSTRACT

The aim of this study was to investigate the use of de-blooming algorithm in coronary CT angiography (CCTA) for optimal evaluation of calcified plaques. Calcified plaques were simulated on a coronary vessel phantom and a cardiac motion phantom. Two convolution kernels, standard (STND) and high-definition standard (HD STND), were used for imaging reconstruction. A dedicated de-blooming algorithm was used for imaging processing. We found a smaller bias towards measurement of stenosis using the de-blooming algorithm (STND: bias 24.6% vs 15.0%, range 10.2% to 39.0% vs 4.0% to 25.9%; HD STND: bias 17.9% vs 11.0%, range 8.9% to 30.6% vs 0.5% to 21.5%). With use of de-blooming algorithm, specificity for diagnosing significant stenosis increased from 45.8% to 75.0% (STND), from 62.5% to 83.3% (HD STND); while positive predictive value (PPV) increased from 69.8% to 83.3% (STND), from 76.9% to 88.2% (HD STND). In the patient group, reduction in calcification volume was 48.1 ± 10.3%, reduction in coronary diameter stenosis over calcified plaque was 52.4 ± 24.2%. Our results suggest that the novel de-blooming algorithm could effectively decrease the blooming artifacts caused by coronary calcified plaques, and consequently improve diagnostic accuracy of CCTA in assessing coronary stenosis. More... »

PAGES

6945

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-25352-5

DOI

http://dx.doi.org/10.1038/s41598-018-25352-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103695437

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29720611


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/2008/1102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Cardiorespiratory Medicine and Haematology", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Capital Medical University", 
          "id": "https://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 100029, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Ping", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Capital Medical University", 
          "id": "https://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 100029, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xu", 
        "givenName": "Lei", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Capital Medical University", 
          "id": "https://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 100029, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yang", 
        "givenName": "Lin", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Capital Medical University", 
          "id": "https://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 100029, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Rui", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "GE Healthcare (United States)", 
          "id": "https://www.grid.ac/institutes/grid.474545.3", 
          "name": [
            "MICT Engineering, GE Healthcare, 53188, Waukesha, WI, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hsieh", 
        "givenName": "Jiang", 
        "id": "sg:person.01100147312.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100147312.12"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Curtin University", 
          "id": "https://www.grid.ac/institutes/grid.1032.0", 
          "name": [
            "Department of Medical Radiation Sciences, Curtin University, 6845, Perth, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sun", 
        "givenName": "Zhonghua", 
        "id": "sg:person.01127454361.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01127454361.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Capital Medical University", 
          "id": "https://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 100029, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fan", 
        "givenName": "Zhanming", 
        "id": "sg:person.01017406664.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01017406664.97"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of British Columbia", 
          "id": "https://www.grid.ac/institutes/grid.17091.3e", 
          "name": [
            "Department of Radiology, St Paul\u2019s Hospital and University of British Columbia, V6Z 1Y6, Vancouver, BC, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Leipsic", 
        "givenName": "Jonathon A.", 
        "id": "sg:person.01037263054.20", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01037263054.20"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1097/md.0000000000002148", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000081601"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/md.0000000000002148", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000081601"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/md.0000000000002148", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000081601"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/atvbaha.113.302642", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001511955"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/atvbaha.113.302642", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001511955"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2009.08.087", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001672414"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejrad.2016.12.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006940218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rct.0b013e3182873559", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008953379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rct.0b013e3182873559", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008953379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcmg.2013.02.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014354510"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2005.09.076", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017069914"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.11103574", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019213993"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2014.07.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021388610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.112.000250", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021636321"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.112.000250", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021636321"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rli.0b013e31819b6fba", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025008352"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rli.0b013e31819b6fba", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025008352"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rli.0b013e31819b6fba", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025008352"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/eurheartj/ehp470", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027183537"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/eurheartj/ehp470", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027183537"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1253/circj.71.643", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029368217"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-011-9902-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031282264", 
          "https://doi.org/10.1007/s10554-011-9902-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2017.01.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034387203"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1253/circj.cj-10-0636", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034968114"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rct.0b013e318282d61c", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041304992"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rct.0b013e318282d61c", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041304992"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcmg.2013.07.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043503381"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2008.07.031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044209235"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2261-11-32", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044449125", 
          "https://doi.org/10.1186/1471-2261-11-32"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.0000145614.07427.9f", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044572759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.13130909", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044575644"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-006-9173-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044862830", 
          "https://doi.org/10.1007/s10554-006-9173-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-006-9173-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044862830", 
          "https://doi.org/10.1007/s10554-006-9173-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcmg.2012.06.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047450285"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2005.03.071", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048922604"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejrad.2010.08.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049005425"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-009-1358-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050794569", 
          "https://doi.org/10.1007/s00330-009-1358-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-009-1358-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050794569", 
          "https://doi.org/10.1007/s00330-009-1358-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-009-1358-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050794569", 
          "https://doi.org/10.1007/s00330-009-1358-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2016.10.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052703534"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.07.4026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069299368"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1077229499", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12410-017-9412-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084035192", 
          "https://doi.org/10.1007/s12410-017-9412-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12410-017-9412-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084035192", 
          "https://doi.org/10.1007/s12410-017-9412-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.116.005893", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085376844"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.116.005893", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085376844"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.116.005893", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085376844"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "The aim of this study was to investigate the use of de-blooming algorithm in coronary CT angiography (CCTA) for optimal evaluation of calcified plaques. Calcified plaques were simulated on a coronary vessel phantom and a cardiac motion phantom. Two convolution kernels, standard (STND) and high-definition standard (HD STND), were used for imaging reconstruction. A dedicated de-blooming algorithm was used for imaging processing. We found a smaller bias towards measurement of stenosis using the de-blooming algorithm (STND: bias 24.6% vs 15.0%, range 10.2% to 39.0% vs 4.0% to 25.9%; HD STND: bias 17.9% vs 11.0%, range 8.9% to 30.6% vs 0.5% to 21.5%). With use of de-blooming algorithm, specificity for diagnosing significant stenosis increased from 45.8% to 75.0% (STND), from 62.5% to 83.3% (HD STND); while positive predictive value (PPV) increased from 69.8% to 83.3% (STND), from 76.9% to 88.2% (HD STND). In the patient group, reduction in calcification volume was 48.1\u2009\u00b1\u200910.3%, reduction in coronary diameter stenosis over calcified plaque was 52.4\u2009\u00b1\u200924.2%. Our results suggest that the novel de-blooming algorithm could effectively decrease the blooming artifacts caused by coronary calcified plaques, and consequently improve diagnostic accuracy of CCTA in assessing coronary stenosis.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41598-018-25352-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7180323", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "8"
      }
    ], 
    "name": "Blooming Artifact Reduction in Coronary Artery Calcification by A New De-blooming Algorithm: Initial Study", 
    "pagination": "6945", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "14b03e2dcb03e06ed365e9429b72387b38c82ae1a18ec3f79822786f7ec039a6"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29720611"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101563288"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41598-018-25352-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1103695437"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41598-018-25352-5", 
      "https://app.dimensions.ai/details/publication/pub.1103695437"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T01:03", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8697_00000494.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41598-018-25352-5"
  }
]
 

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.10.1038/s41598-018-25352-5'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-25352-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-25352-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-25352-5'


 

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

224 TRIPLES      21 PREDICATES      61 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41598-018-25352-5 schema:about anzsrc-for:11
2 anzsrc-for:1102
3 schema:author Na2d4ad88da2f43e89449d04d4582174d
4 schema:citation sg:pub.10.1007/s00330-009-1358-x
5 sg:pub.10.1007/s10554-006-9173-9
6 sg:pub.10.1007/s10554-011-9902-6
7 sg:pub.10.1007/s12410-017-9412-6
8 sg:pub.10.1186/1471-2261-11-32
9 https://app.dimensions.ai/details/publication/pub.1077229499
10 https://doi.org/10.1016/j.ejrad.2010.08.028
11 https://doi.org/10.1016/j.ejrad.2016.12.012
12 https://doi.org/10.1016/j.jacc.2005.03.071
13 https://doi.org/10.1016/j.jacc.2005.09.076
14 https://doi.org/10.1016/j.jacc.2008.07.031
15 https://doi.org/10.1016/j.jacc.2009.08.087
16 https://doi.org/10.1016/j.jcct.2014.07.003
17 https://doi.org/10.1016/j.jcct.2016.10.002
18 https://doi.org/10.1016/j.jcct.2017.01.003
19 https://doi.org/10.1016/j.jcmg.2012.06.006
20 https://doi.org/10.1016/j.jcmg.2013.02.011
21 https://doi.org/10.1016/j.jcmg.2013.07.013
22 https://doi.org/10.1093/eurheartj/ehp470
23 https://doi.org/10.1097/md.0000000000002148
24 https://doi.org/10.1097/rct.0b013e318282d61c
25 https://doi.org/10.1097/rct.0b013e3182873559
26 https://doi.org/10.1097/rli.0b013e31819b6fba
27 https://doi.org/10.1148/radiol.11103574
28 https://doi.org/10.1148/radiol.13130909
29 https://doi.org/10.1161/01.cir.0000145614.07427.9f
30 https://doi.org/10.1161/atvbaha.113.302642
31 https://doi.org/10.1161/circimaging.112.000250
32 https://doi.org/10.1161/circimaging.116.005893
33 https://doi.org/10.1253/circj.71.643
34 https://doi.org/10.1253/circj.cj-10-0636
35 https://doi.org/10.2214/ajr.07.4026
36 schema:datePublished 2018-12
37 schema:datePublishedReg 2018-12-01
38 schema:description The aim of this study was to investigate the use of de-blooming algorithm in coronary CT angiography (CCTA) for optimal evaluation of calcified plaques. Calcified plaques were simulated on a coronary vessel phantom and a cardiac motion phantom. Two convolution kernels, standard (STND) and high-definition standard (HD STND), were used for imaging reconstruction. A dedicated de-blooming algorithm was used for imaging processing. We found a smaller bias towards measurement of stenosis using the de-blooming algorithm (STND: bias 24.6% vs 15.0%, range 10.2% to 39.0% vs 4.0% to 25.9%; HD STND: bias 17.9% vs 11.0%, range 8.9% to 30.6% vs 0.5% to 21.5%). With use of de-blooming algorithm, specificity for diagnosing significant stenosis increased from 45.8% to 75.0% (STND), from 62.5% to 83.3% (HD STND); while positive predictive value (PPV) increased from 69.8% to 83.3% (STND), from 76.9% to 88.2% (HD STND). In the patient group, reduction in calcification volume was 48.1 ± 10.3%, reduction in coronary diameter stenosis over calcified plaque was 52.4 ± 24.2%. Our results suggest that the novel de-blooming algorithm could effectively decrease the blooming artifacts caused by coronary calcified plaques, and consequently improve diagnostic accuracy of CCTA in assessing coronary stenosis.
39 schema:genre research_article
40 schema:inLanguage en
41 schema:isAccessibleForFree true
42 schema:isPartOf Ne6ad9071daf94416b0f8fe715c459a46
43 Nf4c02ebb6fc44ecb899001c5b5f508f8
44 sg:journal.1045337
45 schema:name Blooming Artifact Reduction in Coronary Artery Calcification by A New De-blooming Algorithm: Initial Study
46 schema:pagination 6945
47 schema:productId N0c3be4330a08430dba39a7d7fd6a21ee
48 N16b31b261c5c4c0b95be11addaf48e8b
49 N179f0ed94fa141bbbeb7c6fefefa26f9
50 N77b28ab2e1464accab28df2ae9980f91
51 Nb5ab353ecce946598de9ccf17ec23a8d
52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103695437
53 https://doi.org/10.1038/s41598-018-25352-5
54 schema:sdDatePublished 2019-04-11T01:03
55 schema:sdLicense https://scigraph.springernature.com/explorer/license/
56 schema:sdPublisher N151355f2ff634f41aa02c8555c5a38de
57 schema:url https://www.nature.com/articles/s41598-018-25352-5
58 sgo:license sg:explorer/license/
59 sgo:sdDataset articles
60 rdf:type schema:ScholarlyArticle
61 N0c3be4330a08430dba39a7d7fd6a21ee schema:name readcube_id
62 schema:value 14b03e2dcb03e06ed365e9429b72387b38c82ae1a18ec3f79822786f7ec039a6
63 rdf:type schema:PropertyValue
64 N11bf2317dd07402587f040abe6208bde schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
65 schema:familyName Xu
66 schema:givenName Lei
67 rdf:type schema:Person
68 N151355f2ff634f41aa02c8555c5a38de schema:name Springer Nature - SN SciGraph project
69 rdf:type schema:Organization
70 N16b31b261c5c4c0b95be11addaf48e8b schema:name nlm_unique_id
71 schema:value 101563288
72 rdf:type schema:PropertyValue
73 N179f0ed94fa141bbbeb7c6fefefa26f9 schema:name pubmed_id
74 schema:value 29720611
75 rdf:type schema:PropertyValue
76 N38fc14e6a42544da8700ebc670bb37ca schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
77 schema:familyName Li
78 schema:givenName Ping
79 rdf:type schema:Person
80 N53ddde3c6dc54bfda88f3af2d9d79e53 rdf:first sg:person.01127454361.85
81 rdf:rest Nd9a21d7386c44b899beac72dbcf02b74
82 N77b28ab2e1464accab28df2ae9980f91 schema:name doi
83 schema:value 10.1038/s41598-018-25352-5
84 rdf:type schema:PropertyValue
85 N79317720589249ddaa05f6dab0699638 rdf:first Nce6d185b03424e71bb25ba3e46f78cc1
86 rdf:rest Ne139b3b304cf43308a136dd6f85f3e15
87 N7bf4cb7783be4b9fa708a6dc8373a797 schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
88 schema:familyName Wang
89 schema:givenName Rui
90 rdf:type schema:Person
91 N8a74fb27b9714ada9acba92902f709f9 rdf:first N11bf2317dd07402587f040abe6208bde
92 rdf:rest N79317720589249ddaa05f6dab0699638
93 Na2d4ad88da2f43e89449d04d4582174d rdf:first N38fc14e6a42544da8700ebc670bb37ca
94 rdf:rest N8a74fb27b9714ada9acba92902f709f9
95 Nb5ab353ecce946598de9ccf17ec23a8d schema:name dimensions_id
96 schema:value pub.1103695437
97 rdf:type schema:PropertyValue
98 Nbb4c1f22492b4800b8d0b6a4f551164a rdf:first sg:person.01100147312.12
99 rdf:rest N53ddde3c6dc54bfda88f3af2d9d79e53
100 Nce6d185b03424e71bb25ba3e46f78cc1 schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
101 schema:familyName Yang
102 schema:givenName Lin
103 rdf:type schema:Person
104 Nd75c6c0b644643229323333a205b7f79 rdf:first sg:person.01037263054.20
105 rdf:rest rdf:nil
106 Nd9a21d7386c44b899beac72dbcf02b74 rdf:first sg:person.01017406664.97
107 rdf:rest Nd75c6c0b644643229323333a205b7f79
108 Ne139b3b304cf43308a136dd6f85f3e15 rdf:first N7bf4cb7783be4b9fa708a6dc8373a797
109 rdf:rest Nbb4c1f22492b4800b8d0b6a4f551164a
110 Ne6ad9071daf94416b0f8fe715c459a46 schema:issueNumber 1
111 rdf:type schema:PublicationIssue
112 Nf4c02ebb6fc44ecb899001c5b5f508f8 schema:volumeNumber 8
113 rdf:type schema:PublicationVolume
114 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
115 schema:name Medical and Health Sciences
116 rdf:type schema:DefinedTerm
117 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
118 schema:name Cardiorespiratory Medicine and Haematology
119 rdf:type schema:DefinedTerm
120 sg:grant.7180323 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-018-25352-5
121 rdf:type schema:MonetaryGrant
122 sg:journal.1045337 schema:issn 2045-2322
123 schema:name Scientific Reports
124 rdf:type schema:Periodical
125 sg:person.01017406664.97 schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
126 schema:familyName Fan
127 schema:givenName Zhanming
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01017406664.97
129 rdf:type schema:Person
130 sg:person.01037263054.20 schema:affiliation https://www.grid.ac/institutes/grid.17091.3e
131 schema:familyName Leipsic
132 schema:givenName Jonathon A.
133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01037263054.20
134 rdf:type schema:Person
135 sg:person.01100147312.12 schema:affiliation https://www.grid.ac/institutes/grid.474545.3
136 schema:familyName Hsieh
137 schema:givenName Jiang
138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100147312.12
139 rdf:type schema:Person
140 sg:person.01127454361.85 schema:affiliation https://www.grid.ac/institutes/grid.1032.0
141 schema:familyName Sun
142 schema:givenName Zhonghua
143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01127454361.85
144 rdf:type schema:Person
145 sg:pub.10.1007/s00330-009-1358-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1050794569
146 https://doi.org/10.1007/s00330-009-1358-x
147 rdf:type schema:CreativeWork
148 sg:pub.10.1007/s10554-006-9173-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044862830
149 https://doi.org/10.1007/s10554-006-9173-9
150 rdf:type schema:CreativeWork
151 sg:pub.10.1007/s10554-011-9902-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031282264
152 https://doi.org/10.1007/s10554-011-9902-6
153 rdf:type schema:CreativeWork
154 sg:pub.10.1007/s12410-017-9412-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084035192
155 https://doi.org/10.1007/s12410-017-9412-6
156 rdf:type schema:CreativeWork
157 sg:pub.10.1186/1471-2261-11-32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044449125
158 https://doi.org/10.1186/1471-2261-11-32
159 rdf:type schema:CreativeWork
160 https://app.dimensions.ai/details/publication/pub.1077229499 schema:CreativeWork
161 https://doi.org/10.1016/j.ejrad.2010.08.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049005425
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.ejrad.2016.12.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006940218
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1016/j.jacc.2005.03.071 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048922604
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/j.jacc.2005.09.076 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017069914
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1016/j.jacc.2008.07.031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044209235
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/j.jacc.2009.08.087 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001672414
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/j.jcct.2014.07.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021388610
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/j.jcct.2016.10.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052703534
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/j.jcct.2017.01.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034387203
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1016/j.jcmg.2012.06.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047450285
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1016/j.jcmg.2013.02.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014354510
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1016/j.jcmg.2013.07.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043503381
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1093/eurheartj/ehp470 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027183537
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1097/md.0000000000002148 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000081601
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1097/rct.0b013e318282d61c schema:sameAs https://app.dimensions.ai/details/publication/pub.1041304992
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1097/rct.0b013e3182873559 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008953379
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1097/rli.0b013e31819b6fba schema:sameAs https://app.dimensions.ai/details/publication/pub.1025008352
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1148/radiol.11103574 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019213993
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1148/radiol.13130909 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044575644
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1161/01.cir.0000145614.07427.9f schema:sameAs https://app.dimensions.ai/details/publication/pub.1044572759
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1161/atvbaha.113.302642 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001511955
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1161/circimaging.112.000250 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021636321
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1161/circimaging.116.005893 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085376844
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1253/circj.71.643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029368217
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1253/circj.cj-10-0636 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034968114
210 rdf:type schema:CreativeWork
211 https://doi.org/10.2214/ajr.07.4026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069299368
212 rdf:type schema:CreativeWork
213 https://www.grid.ac/institutes/grid.1032.0 schema:alternateName Curtin University
214 schema:name Department of Medical Radiation Sciences, Curtin University, 6845, Perth, Australia
215 rdf:type schema:Organization
216 https://www.grid.ac/institutes/grid.17091.3e schema:alternateName University of British Columbia
217 schema:name Department of Radiology, St Paul’s Hospital and University of British Columbia, V6Z 1Y6, Vancouver, BC, Canada
218 rdf:type schema:Organization
219 https://www.grid.ac/institutes/grid.24696.3f schema:alternateName Capital Medical University
220 schema:name Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 100029, Beijing, China
221 rdf:type schema:Organization
222 https://www.grid.ac/institutes/grid.474545.3 schema:alternateName GE Healthcare (United States)
223 schema:name MICT Engineering, GE Healthcare, 53188, Waukesha, WI, USA
224 rdf:type schema:Organization
 




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


...