Prospective ECG-gated 320 row detector computed tomography: implications for CT angiography and perfusion imaging View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2009-08

AUTHORS

Kakuya Kitagawa, Albert C. Lardo, Joao A. C. Lima, Richard T. George

ABSTRACT

Cardiac multidetector computed tomography has evolved from early four detector systems that first demonstrated the feasibility of non-invasive angiography to today’s wide-area detector computed tomography systems, such as 320-row detector computed tomography. As detector arrays have widened, there have been great improvements in image quality that have improved test accuracy. In addition, wider detector arrays have allowed for the application of prospective ECG-gating for CT angiography, although the current 64-row detector systems have some limitations. 320-row detector computed tomography with full cardiac coverage allows for cardiac imaging in a single heart beat. This technology has realized some of the great advantages provided by full cardiac coverage in regards to image quality (elimination of step artifacts and variation in contrast enhancement), patient safety (reductions in overall radiation and contrast dose), and the prospects for combined CT angiography and myocardial perfusion imaging are very promising. We will review the technical aspects of 320-row detector computed tomography and their implications for coronary angiography and perfusion imaging. More... »

PAGES

201

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10554-009-9433-6

DOI

http://dx.doi.org/10.1007/s10554-009-9433-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025927070


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/2008/0299", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Other Physical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/02", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Mie University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412075.5", 
          "name": [
            "Department of Diagnostic Radiology, Mie University Hospital, Tsu City, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kitagawa", 
        "givenName": "Kakuya", 
        "id": "sg:person.0763634731.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0763634731.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Johns Hopkins University", 
          "id": "https://www.grid.ac/institutes/grid.21107.35", 
          "name": [
            "Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, 720 Rutland Street, Ross Building 1042, 21205, Baltimore, MD, USA", 
            "Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lardo", 
        "givenName": "Albert C.", 
        "id": "sg:person.01062150525.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01062150525.82"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Johns Hopkins University", 
          "id": "https://www.grid.ac/institutes/grid.21107.35", 
          "name": [
            "Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Blalock Building 524, 21207, Baltimore, MD, USA", 
            "Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lima", 
        "givenName": "Joao A. C.", 
        "id": "sg:person.016123542477.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016123542477.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Johns Hopkins University", 
          "id": "https://www.grid.ac/institutes/grid.21107.35", 
          "name": [
            "Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Carnegie Building 568, 21287, Baltimore, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "George", 
        "givenName": "Richard T.", 
        "id": "sg:person.01100661206.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100661206.03"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.jacc.2005.05.056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002160027"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.296.4.403", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004178316"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-008-9347-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010389969", 
          "https://doi.org/10.1007/s10554-008-9347-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-008-9347-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010389969", 
          "https://doi.org/10.1007/s10554-008-9347-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/eurheartj/ehm613", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022130810"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.rct.0000173844.89988.37", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024420708"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.rct.0000173844.89988.37", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024420708"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.rct.0000173844.89988.37", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024420708"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-008-9308-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026084106", 
          "https://doi.org/10.1007/s10554-008-9308-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejrad.2007.05.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031398421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2006.04.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033946852"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rli.0b013e318124a884", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045237062"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rli.0b013e318124a884", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045237062"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.102.23.2823", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048576794"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2442061218", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053343799"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/51/16/005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059026166"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1259/bjr/39775216", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064569254"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/02841850500479669", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077220004"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/02841850500479669", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077220004"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/02841850500479669", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077220004"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2009-08", 
    "datePublishedReg": "2009-08-01", 
    "description": "Cardiac multidetector computed tomography has evolved from early four detector systems that first demonstrated the feasibility of non-invasive angiography to today\u2019s wide-area detector computed tomography systems, such as 320-row detector computed tomography. As detector arrays have widened, there have been great improvements in image quality that have improved test accuracy. In addition, wider detector arrays have allowed for the application of prospective ECG-gating for CT angiography, although the current 64-row detector systems have some limitations. 320-row detector computed tomography with full cardiac coverage allows for cardiac imaging in a single heart beat. This technology has realized some of the great advantages provided by full cardiac coverage in regards to image quality (elimination of step artifacts and variation in contrast enhancement), patient safety (reductions in overall radiation and contrast dose), and the prospects for combined CT angiography and myocardial perfusion imaging are very promising. We will review the technical aspects of 320-row detector computed tomography and their implications for coronary angiography and perfusion imaging.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10554-009-9433-6", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1025429", 
        "issn": [
          "1569-5794", 
          "1573-0743"
        ], 
        "name": "The International Journal of Cardiovascular Imaging", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "Suppl 2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "25"
      }
    ], 
    "name": "Prospective ECG-gated 320 row detector computed tomography: implications for CT angiography and perfusion imaging", 
    "pagination": "201", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "7b846b083314245dbe5208ca3663209e064ded1ad4cbe6f634934a359932cb9a"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10554-009-9433-6"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1025927070"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10554-009-9433-6", 
      "https://app.dimensions.ai/details/publication/pub.1025927070"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T14:26", 
    "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/v90/0000000373_0000000373/records_13071_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs10554-009-9433-6"
  }
]
 

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

HOW TO GET THIS DATA PROGRAMMATICALLY:

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

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10554-009-9433-6'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10554-009-9433-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10554-009-9433-6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10554-009-9433-6'


 

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

133 TRIPLES      21 PREDICATES      41 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10554-009-9433-6 schema:about anzsrc-for:02
2 anzsrc-for:0299
3 schema:author N6356a4f0d0b940ef8235f5ca21c01864
4 schema:citation sg:pub.10.1007/s10554-008-9308-2
5 sg:pub.10.1007/s10554-008-9347-8
6 https://doi.org/10.1001/jama.296.4.403
7 https://doi.org/10.1016/j.ejrad.2007.05.001
8 https://doi.org/10.1016/j.jacc.2005.05.056
9 https://doi.org/10.1016/j.jacc.2006.04.014
10 https://doi.org/10.1080/02841850500479669
11 https://doi.org/10.1088/0031-9155/51/16/005
12 https://doi.org/10.1093/eurheartj/ehm613
13 https://doi.org/10.1097/01.rct.0000173844.89988.37
14 https://doi.org/10.1097/rli.0b013e318124a884
15 https://doi.org/10.1148/radiol.2442061218
16 https://doi.org/10.1161/01.cir.102.23.2823
17 https://doi.org/10.1259/bjr/39775216
18 schema:datePublished 2009-08
19 schema:datePublishedReg 2009-08-01
20 schema:description Cardiac multidetector computed tomography has evolved from early four detector systems that first demonstrated the feasibility of non-invasive angiography to today’s wide-area detector computed tomography systems, such as 320-row detector computed tomography. As detector arrays have widened, there have been great improvements in image quality that have improved test accuracy. In addition, wider detector arrays have allowed for the application of prospective ECG-gating for CT angiography, although the current 64-row detector systems have some limitations. 320-row detector computed tomography with full cardiac coverage allows for cardiac imaging in a single heart beat. This technology has realized some of the great advantages provided by full cardiac coverage in regards to image quality (elimination of step artifacts and variation in contrast enhancement), patient safety (reductions in overall radiation and contrast dose), and the prospects for combined CT angiography and myocardial perfusion imaging are very promising. We will review the technical aspects of 320-row detector computed tomography and their implications for coronary angiography and perfusion imaging.
21 schema:genre research_article
22 schema:inLanguage en
23 schema:isAccessibleForFree false
24 schema:isPartOf N0d1c9de5588d43b596377ba7ffd99fcd
25 N8a3835be4a934abeb575f5846c43aceb
26 sg:journal.1025429
27 schema:name Prospective ECG-gated 320 row detector computed tomography: implications for CT angiography and perfusion imaging
28 schema:pagination 201
29 schema:productId N0ea075d68a624adf9ef935302dd62da7
30 N3c44b8c4dfbd45dd854032713dd66418
31 N6340c76f22564429b63f939e300cfdd8
32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025927070
33 https://doi.org/10.1007/s10554-009-9433-6
34 schema:sdDatePublished 2019-04-11T14:26
35 schema:sdLicense https://scigraph.springernature.com/explorer/license/
36 schema:sdPublisher N5f8ba7f9bbd84e6bb9beb1c93f7128f8
37 schema:url http://link.springer.com/10.1007%2Fs10554-009-9433-6
38 sgo:license sg:explorer/license/
39 sgo:sdDataset articles
40 rdf:type schema:ScholarlyArticle
41 N0d1c9de5588d43b596377ba7ffd99fcd schema:issueNumber Suppl 2
42 rdf:type schema:PublicationIssue
43 N0ea075d68a624adf9ef935302dd62da7 schema:name readcube_id
44 schema:value 7b846b083314245dbe5208ca3663209e064ded1ad4cbe6f634934a359932cb9a
45 rdf:type schema:PropertyValue
46 N2b9933897ff3428fbfe9ca1587e66d2e rdf:first sg:person.01062150525.82
47 rdf:rest N92c8b556c4ff4438874522976d9c337b
48 N3c44b8c4dfbd45dd854032713dd66418 schema:name dimensions_id
49 schema:value pub.1025927070
50 rdf:type schema:PropertyValue
51 N4cf3efb24b5b4cefb0ca0f4171e65dc4 rdf:first sg:person.01100661206.03
52 rdf:rest rdf:nil
53 N5f8ba7f9bbd84e6bb9beb1c93f7128f8 schema:name Springer Nature - SN SciGraph project
54 rdf:type schema:Organization
55 N6340c76f22564429b63f939e300cfdd8 schema:name doi
56 schema:value 10.1007/s10554-009-9433-6
57 rdf:type schema:PropertyValue
58 N6356a4f0d0b940ef8235f5ca21c01864 rdf:first sg:person.0763634731.07
59 rdf:rest N2b9933897ff3428fbfe9ca1587e66d2e
60 N8a3835be4a934abeb575f5846c43aceb schema:volumeNumber 25
61 rdf:type schema:PublicationVolume
62 N92c8b556c4ff4438874522976d9c337b rdf:first sg:person.016123542477.52
63 rdf:rest N4cf3efb24b5b4cefb0ca0f4171e65dc4
64 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
65 schema:name Physical Sciences
66 rdf:type schema:DefinedTerm
67 anzsrc-for:0299 schema:inDefinedTermSet anzsrc-for:
68 schema:name Other Physical Sciences
69 rdf:type schema:DefinedTerm
70 sg:journal.1025429 schema:issn 1569-5794
71 1573-0743
72 schema:name The International Journal of Cardiovascular Imaging
73 rdf:type schema:Periodical
74 sg:person.01062150525.82 schema:affiliation https://www.grid.ac/institutes/grid.21107.35
75 schema:familyName Lardo
76 schema:givenName Albert C.
77 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01062150525.82
78 rdf:type schema:Person
79 sg:person.01100661206.03 schema:affiliation https://www.grid.ac/institutes/grid.21107.35
80 schema:familyName George
81 schema:givenName Richard T.
82 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100661206.03
83 rdf:type schema:Person
84 sg:person.016123542477.52 schema:affiliation https://www.grid.ac/institutes/grid.21107.35
85 schema:familyName Lima
86 schema:givenName Joao A. C.
87 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016123542477.52
88 rdf:type schema:Person
89 sg:person.0763634731.07 schema:affiliation https://www.grid.ac/institutes/grid.412075.5
90 schema:familyName Kitagawa
91 schema:givenName Kakuya
92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0763634731.07
93 rdf:type schema:Person
94 sg:pub.10.1007/s10554-008-9308-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026084106
95 https://doi.org/10.1007/s10554-008-9308-2
96 rdf:type schema:CreativeWork
97 sg:pub.10.1007/s10554-008-9347-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010389969
98 https://doi.org/10.1007/s10554-008-9347-8
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1001/jama.296.4.403 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004178316
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1016/j.ejrad.2007.05.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031398421
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1016/j.jacc.2005.05.056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002160027
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1016/j.jacc.2006.04.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033946852
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1080/02841850500479669 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077220004
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1088/0031-9155/51/16/005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059026166
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1093/eurheartj/ehm613 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022130810
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1097/01.rct.0000173844.89988.37 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024420708
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1097/rli.0b013e318124a884 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045237062
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1148/radiol.2442061218 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053343799
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1161/01.cir.102.23.2823 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048576794
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1259/bjr/39775216 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064569254
123 rdf:type schema:CreativeWork
124 https://www.grid.ac/institutes/grid.21107.35 schema:alternateName Johns Hopkins University
125 schema:name Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
126 Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Blalock Building 524, 21207, Baltimore, MD, USA
127 Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Carnegie Building 568, 21287, Baltimore, MD, USA
128 Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, 720 Rutland Street, Ross Building 1042, 21205, Baltimore, MD, USA
129 Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
130 rdf:type schema:Organization
131 https://www.grid.ac/institutes/grid.412075.5 schema:alternateName Mie University Hospital
132 schema:name Department of Diagnostic Radiology, Mie University Hospital, Tsu City, Japan
133 rdf:type schema:Organization
 




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


...