320-row CT coronary angiography: effect of 100-kV tube voltages on image quality, contrast volume, and radiation dose View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2011-10

AUTHORS

Chuanchen Zhang, Zhaoqi Zhang, Zixu Yan, Lei Xu, Wei Yu, Rui Wang

ABSTRACT

To prospectively evaluate image quality parameters, contrast volume and radiation dose at the 100-kilovolt (kV) setting during coronary computed tomographic angiography (CCTA) on a 320-row computed tomography scanner. We enrolled 107 consecutive patients with a heart rate <65 beats per minute (bpm) undergoing prospective electrocardiogram (ECG)-triggered CCTA. Forty patients with a body mass index (BMI) <25 kg/m(2) were scanned using 100-kV tube voltage settings, while 67 patients were scanned using 120-kV protocols. Image quality was assessed by two readers unaware of patient information and scan parameters. Attenuation in the aorta and perivascular fat tissue and image noise were measured. Contrast-to-noise ratios (CNRs) and contrast material volumes were calculated. The effective radiation doses were estimated using a chest conversion coefficient (0.017). Diagnostic image quality was achieved in 98.2% of coronary segments with 100-kV CCTA and 98.6% of coronary segments with 120-kV CCTA, with no significant differences in image quality scores for each coronary segment. Vessel attenuation, image noise, and CNR were not significantly different between the 100- and 120-kV protocols. Mean contrast injection rate and mean material volume were significantly lower for the 100-kV CCTA (4.35 ± 0.28 ml/s and 53.13 ± 3.77 ml, respectively) than for the 120-kV CCTA (5.16 ± 0.21 ml/s and 62.40 ± 3.66 ml respectively; P < 0.001). The effective radiation dose was 2.12 ± 0.19 mSv for 100-kV CCTA, a reduction of 54% compared to 4.61 ± 0.82 mSv for 120-kV CCTA. A 100-kV CCTA can be implemented in patients with a BMI < 25 kg/m(2). The 100-kV setting allows significant reductions in contrast material volume and effective radiation dose while maintaining adequate diagnostic image quality. More... »

PAGES

1059-1068

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10554-010-9754-5

DOI

http://dx.doi.org/10.1007/s10554-010-9754-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042620284

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/21110100


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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Body Mass Index", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cardiac-Gated Imaging Techniques", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Chi-Square Distribution", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "China", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Contrast Media", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Coronary Angiography", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Coronary Artery Disease", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Electrocardiography", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Feasibility Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Heart Rate", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "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": "Predictive Value of Tests", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Prospective Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiation Dosage", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiographic Image Interpretation, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, X-Ray Computed", 
        "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, 2 Anzhen Road, 100029, Chaoyang District, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Chuanchen", 
        "id": "sg:person.01105702465.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01105702465.29"
        ], 
        "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, 2 Anzhen Road, 100029, Chaoyang District, Beijing, People\u2019s Republic of 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": "Capital Medical University", 
          "id": "https://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, 100029, Chaoyang District, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yan", 
        "givenName": "Zixu", 
        "id": "sg:person.01027402475.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027402475.85"
        ], 
        "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, 2 Anzhen Road, 100029, Chaoyang District, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xu", 
        "givenName": "Lei", 
        "id": "sg:person.0713154075.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0713154075.07"
        ], 
        "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, 2 Anzhen Road, 100029, Chaoyang District, Beijing, People\u2019s Republic of 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": "Capital Medical University", 
          "id": "https://www.grid.ac/institutes/grid.24696.3f", 
          "name": [
            "Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, 100029, Chaoyang District, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Rui", 
        "id": "sg:person.0642401716.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0642401716.48"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1161/circulationaha.106.634808", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001613860"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "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/10.1097/rct.0b013e31815ea873", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005381836"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rct.0b013e31815ea873", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005381836"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-009-1692-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010453428", 
          "https://doi.org/10.1007/s00330-009-1692-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-009-1692-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010453428", 
          "https://doi.org/10.1007/s00330-009-1692-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2008.07.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017395017"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-008-0966-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023746890", 
          "https://doi.org/10.1007/s00330-008-0966-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-008-0966-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023746890", 
          "https://doi.org/10.1007/s00330-008-0966-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2482072192", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024935051"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-009-9433-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025927070", 
          "https://doi.org/10.1007/s10554-009-9433-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-009-9433-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025927070", 
          "https://doi.org/10.1007/s10554-009-9433-6"
        ], 
        "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.2009.07.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026530090"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcmg.2007.11.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026657493"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.2009.54", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026937699"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2008.05.146", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027122555"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2009.05.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027354141"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-007-0786-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028078235", 
          "https://doi.org/10.1007/s00330-007-0786-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-007-0786-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028078235", 
          "https://doi.org/10.1007/s00330-007-0786-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrcardio.2009.53", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028751002", 
          "https://doi.org/10.1038/nrcardio.2009.53"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcmg.2009.02.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030103166"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circulationaha.105.602490", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030836310"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.acra.2009.09.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031111467"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2483072032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032386157"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circulationaha.105.533471", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032983112"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.rct.0000236422.35761.a1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034755139"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/01.rct.0000236422.35761.a1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034755139"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcct.2008.12.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035503600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.298.3.317", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036295152"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.0000048965.56529.c2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036313717"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2463070989", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037037103"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/eurheartj/ehp571", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037926070"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2215/cjn.05200709", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037961409"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2531090065", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040898294"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-009-9535-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044896001", 
          "https://doi.org/10.1007/s10554-009-9535-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-009-9535-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044896001", 
          "https://doi.org/10.1007/s10554-009-9535-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-009-9535-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044896001", 
          "https://doi.org/10.1007/s10554-009-9535-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2311030191", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045870566"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2009.04.027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046273989"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rli.0b013e31803b93cf", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049206841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rli.0b013e31803b93cf", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049206841"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circulationaha.109.859280", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053471912"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.51.4.5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063335684"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1259/bjr/66519303", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064569910"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.05.0216", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069297467"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.07.3124", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069299006"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.08.1347", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069299651"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.09.3543", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069300529"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2011-10", 
    "datePublishedReg": "2011-10-01", 
    "description": "To prospectively evaluate image quality parameters, contrast volume and radiation dose at the 100-kilovolt (kV) setting during coronary computed tomographic angiography (CCTA) on a 320-row computed tomography scanner. We enrolled 107 consecutive patients with a heart rate <65 beats per minute (bpm) undergoing prospective electrocardiogram (ECG)-triggered CCTA. Forty patients with a body mass index (BMI) <25\u00a0kg/m(2) were scanned using 100-kV tube voltage settings, while 67 patients were scanned using 120-kV protocols. Image quality was assessed by two readers unaware of patient information and scan parameters. Attenuation in the aorta and perivascular fat tissue and image noise were measured. Contrast-to-noise ratios (CNRs) and contrast material volumes were calculated. The effective radiation doses were estimated using a chest conversion coefficient (0.017). Diagnostic image quality was achieved in 98.2% of coronary segments with 100-kV CCTA and 98.6% of coronary segments with 120-kV CCTA, with no significant differences in image quality scores for each coronary segment. Vessel attenuation, image noise, and CNR were not significantly different between the 100- and 120-kV protocols. Mean contrast injection rate and mean material volume were significantly lower for the 100-kV CCTA (4.35\u00a0\u00b1\u00a00.28\u00a0ml/s and 53.13\u00a0\u00b1\u00a03.77\u00a0ml, respectively) than for the 120-kV CCTA (5.16\u00a0\u00b1\u00a00.21\u00a0ml/s and 62.40\u00a0\u00b1\u00a03.66\u00a0ml respectively; P\u00a0<\u00a00.001). The effective radiation dose was 2.12\u00a0\u00b1\u00a00.19\u00a0mSv for 100-kV CCTA, a reduction of 54% compared to 4.61\u00a0\u00b1\u00a00.82\u00a0mSv for 120-kV CCTA. A 100-kV CCTA can be implemented in patients with a BMI\u00a0<\u00a025\u00a0kg/m(2). The 100-kV setting allows significant reductions in contrast material volume and effective radiation dose while maintaining adequate diagnostic image quality.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10554-010-9754-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1025429", 
        "issn": [
          "1569-5794", 
          "1573-0743"
        ], 
        "name": "The International Journal of Cardiovascular Imaging", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "7", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "27"
      }
    ], 
    "name": "320-row CT coronary angiography: effect of 100-kV tube voltages on image quality, contrast volume, and radiation dose", 
    "pagination": "1059-1068", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "cb049bf7a0349323945efdf7e9a79a20c8fa0f76eba8c3f35c877b1bce29a59a"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "21110100"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100969716"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10554-010-9754-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1042620284"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10554-010-9754-5", 
      "https://app.dimensions.ai/details/publication/pub.1042620284"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T01:00", 
    "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_00000482.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s10554-010-9754-5"
  }
]
 

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.10.1007/s10554-010-9754-5'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10554-010-9754-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10554-010-9754-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10554-010-9754-5'


 

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

311 TRIPLES      21 PREDICATES      89 URIs      41 LITERALS      29 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10554-010-9754-5 schema:about N135bbb693ad048d6842ac744314f9736
2 N15e5173f9a014e28bff61d280641d723
3 N2a264928c8a24bb4a7f9f7c83af15e6f
4 N3192f67a55b446b0825f2be751b02b85
5 N396eb458eae24e1d98fbf2240c92de1b
6 N456492da3891443db13af773c5b64124
7 N67be5995aa0744d295de26e22c439095
8 N72dd7660508a4b82b5f17ee5cd01fd11
9 N8e4319e800ca40008b1d50c086aa6687
10 N9320615e88484a8da8b1d59b46f67724
11 N9e84e1e5c91845b58f7b12d6d7e3bb4b
12 Na468f40868e741c79d408645b4f22413
13 Nb0115da6b2404d3c94d77d659a98af0e
14 Nb61f3556808f434db185cabdcdfd140a
15 Nc318d789ad9647fcba4842fefb71e3eb
16 Nd79e8c2edd6741ce8d251635d5b4cb34
17 Ndebbbb27f58d4fbc82c136e28ebd43b1
18 Ne08a49b6f88044bea386761556455800
19 Nee38d04fff2e4e6a90c434cd95cb69cb
20 Nf582edcd76324273816a5c8dfd94ca52
21 anzsrc-for:02
22 anzsrc-for:0299
23 schema:author N28cfc0c585d14c6e9baf6909f61772f7
24 schema:citation sg:pub.10.1007/s00330-007-0786-8
25 sg:pub.10.1007/s00330-008-0966-1
26 sg:pub.10.1007/s00330-009-1692-z
27 sg:pub.10.1007/s10554-008-9308-2
28 sg:pub.10.1007/s10554-009-9433-6
29 sg:pub.10.1007/s10554-009-9535-1
30 sg:pub.10.1038/nrcardio.2009.53
31 https://doi.org/10.1001/jama.2009.54
32 https://doi.org/10.1001/jama.298.3.317
33 https://doi.org/10.1016/j.acra.2009.09.010
34 https://doi.org/10.1016/j.ejrad.2009.07.012
35 https://doi.org/10.1016/j.jacc.2005.05.056
36 https://doi.org/10.1016/j.jacc.2009.04.027
37 https://doi.org/10.1016/j.jcct.2008.05.146
38 https://doi.org/10.1016/j.jcct.2008.07.003
39 https://doi.org/10.1016/j.jcct.2008.12.010
40 https://doi.org/10.1016/j.jcct.2009.05.013
41 https://doi.org/10.1016/j.jcmg.2007.11.006
42 https://doi.org/10.1016/j.jcmg.2009.02.015
43 https://doi.org/10.1093/eurheartj/ehp571
44 https://doi.org/10.1097/01.rct.0000236422.35761.a1
45 https://doi.org/10.1097/rct.0b013e31815ea873
46 https://doi.org/10.1097/rli.0b013e31803b93cf
47 https://doi.org/10.1148/radiol.2311030191
48 https://doi.org/10.1148/radiol.2463070989
49 https://doi.org/10.1148/radiol.2482072192
50 https://doi.org/10.1148/radiol.2483072032
51 https://doi.org/10.1148/radiol.2531090065
52 https://doi.org/10.1161/01.cir.0000048965.56529.c2
53 https://doi.org/10.1161/01.cir.51.4.5
54 https://doi.org/10.1161/circulationaha.105.533471
55 https://doi.org/10.1161/circulationaha.105.602490
56 https://doi.org/10.1161/circulationaha.106.634808
57 https://doi.org/10.1161/circulationaha.109.859280
58 https://doi.org/10.1259/bjr/66519303
59 https://doi.org/10.2214/ajr.05.0216
60 https://doi.org/10.2214/ajr.07.3124
61 https://doi.org/10.2214/ajr.08.1347
62 https://doi.org/10.2214/ajr.09.3543
63 https://doi.org/10.2215/cjn.05200709
64 schema:datePublished 2011-10
65 schema:datePublishedReg 2011-10-01
66 schema:description To prospectively evaluate image quality parameters, contrast volume and radiation dose at the 100-kilovolt (kV) setting during coronary computed tomographic angiography (CCTA) on a 320-row computed tomography scanner. We enrolled 107 consecutive patients with a heart rate <65 beats per minute (bpm) undergoing prospective electrocardiogram (ECG)-triggered CCTA. Forty patients with a body mass index (BMI) <25 kg/m(2) were scanned using 100-kV tube voltage settings, while 67 patients were scanned using 120-kV protocols. Image quality was assessed by two readers unaware of patient information and scan parameters. Attenuation in the aorta and perivascular fat tissue and image noise were measured. Contrast-to-noise ratios (CNRs) and contrast material volumes were calculated. The effective radiation doses were estimated using a chest conversion coefficient (0.017). Diagnostic image quality was achieved in 98.2% of coronary segments with 100-kV CCTA and 98.6% of coronary segments with 120-kV CCTA, with no significant differences in image quality scores for each coronary segment. Vessel attenuation, image noise, and CNR were not significantly different between the 100- and 120-kV protocols. Mean contrast injection rate and mean material volume were significantly lower for the 100-kV CCTA (4.35 ± 0.28 ml/s and 53.13 ± 3.77 ml, respectively) than for the 120-kV CCTA (5.16 ± 0.21 ml/s and 62.40 ± 3.66 ml respectively; P < 0.001). The effective radiation dose was 2.12 ± 0.19 mSv for 100-kV CCTA, a reduction of 54% compared to 4.61 ± 0.82 mSv for 120-kV CCTA. A 100-kV CCTA can be implemented in patients with a BMI < 25 kg/m(2). The 100-kV setting allows significant reductions in contrast material volume and effective radiation dose while maintaining adequate diagnostic image quality.
67 schema:genre research_article
68 schema:inLanguage en
69 schema:isAccessibleForFree false
70 schema:isPartOf N3829cd0806fe4d1c9e694b9190ad482c
71 N969f7558145f42aaa89d7301f7d6b1c3
72 sg:journal.1025429
73 schema:name 320-row CT coronary angiography: effect of 100-kV tube voltages on image quality, contrast volume, and radiation dose
74 schema:pagination 1059-1068
75 schema:productId N0bfb9c0f0fcd40fb8828c2bff01800cf
76 N816fba97a16c40b4b45e5a329e979c4e
77 N84e62972a23c49ef8cf8d92fcd1f6375
78 Nae8c0cffe96e491fb9e214e82354ebe0
79 Nd2f77ca67ad84de1b8c47e89bc3a8cf7
80 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042620284
81 https://doi.org/10.1007/s10554-010-9754-5
82 schema:sdDatePublished 2019-04-11T01:00
83 schema:sdLicense https://scigraph.springernature.com/explorer/license/
84 schema:sdPublisher N124cfcc6abfd4572b94f5de062de107f
85 schema:url http://link.springer.com/10.1007/s10554-010-9754-5
86 sgo:license sg:explorer/license/
87 sgo:sdDataset articles
88 rdf:type schema:ScholarlyArticle
89 N0bfb9c0f0fcd40fb8828c2bff01800cf schema:name doi
90 schema:value 10.1007/s10554-010-9754-5
91 rdf:type schema:PropertyValue
92 N124cfcc6abfd4572b94f5de062de107f schema:name Springer Nature - SN SciGraph project
93 rdf:type schema:Organization
94 N135bbb693ad048d6842ac744314f9736 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
95 schema:name Radiographic Image Interpretation, Computer-Assisted
96 rdf:type schema:DefinedTerm
97 N15e5173f9a014e28bff61d280641d723 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
98 schema:name China
99 rdf:type schema:DefinedTerm
100 N28cfc0c585d14c6e9baf6909f61772f7 rdf:first sg:person.01105702465.29
101 rdf:rest Nb7b562e236e74d249d2e6c6d6a1c88a6
102 N29c4f65d5e5e4ed699806e81ba35b7b6 rdf:first sg:person.01143631075.45
103 rdf:rest Nb449a597df6a4d4484c7eefc2a179803
104 N2a264928c8a24bb4a7f9f7c83af15e6f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
105 schema:name Humans
106 rdf:type schema:DefinedTerm
107 N3192f67a55b446b0825f2be751b02b85 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
108 schema:name Radiation Dosage
109 rdf:type schema:DefinedTerm
110 N33b2a34d89624c1a84b352b980a52ed3 rdf:first sg:person.01027402475.85
111 rdf:rest Nc258795e32b24689b44882f2feea0da7
112 N3829cd0806fe4d1c9e694b9190ad482c schema:issueNumber 7
113 rdf:type schema:PublicationIssue
114 N396eb458eae24e1d98fbf2240c92de1b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
115 schema:name Electrocardiography
116 rdf:type schema:DefinedTerm
117 N456492da3891443db13af773c5b64124 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Prospective Studies
119 rdf:type schema:DefinedTerm
120 N67be5995aa0744d295de26e22c439095 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
121 schema:name Tomography, X-Ray Computed
122 rdf:type schema:DefinedTerm
123 N72dd7660508a4b82b5f17ee5cd01fd11 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
124 schema:name Middle Aged
125 rdf:type schema:DefinedTerm
126 N816fba97a16c40b4b45e5a329e979c4e schema:name dimensions_id
127 schema:value pub.1042620284
128 rdf:type schema:PropertyValue
129 N84e62972a23c49ef8cf8d92fcd1f6375 schema:name pubmed_id
130 schema:value 21110100
131 rdf:type schema:PropertyValue
132 N8e4319e800ca40008b1d50c086aa6687 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
133 schema:name Feasibility Studies
134 rdf:type schema:DefinedTerm
135 N9320615e88484a8da8b1d59b46f67724 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Aged
137 rdf:type schema:DefinedTerm
138 N969f7558145f42aaa89d7301f7d6b1c3 schema:volumeNumber 27
139 rdf:type schema:PublicationVolume
140 N9e84e1e5c91845b58f7b12d6d7e3bb4b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
141 schema:name Coronary Artery Disease
142 rdf:type schema:DefinedTerm
143 Na468f40868e741c79d408645b4f22413 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
144 schema:name Predictive Value of Tests
145 rdf:type schema:DefinedTerm
146 Nae8c0cffe96e491fb9e214e82354ebe0 schema:name readcube_id
147 schema:value cb049bf7a0349323945efdf7e9a79a20c8fa0f76eba8c3f35c877b1bce29a59a
148 rdf:type schema:PropertyValue
149 Nb0115da6b2404d3c94d77d659a98af0e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
150 schema:name Contrast Media
151 rdf:type schema:DefinedTerm
152 Nb449a597df6a4d4484c7eefc2a179803 rdf:first sg:person.0642401716.48
153 rdf:rest rdf:nil
154 Nb61f3556808f434db185cabdcdfd140a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
155 schema:name Heart Rate
156 rdf:type schema:DefinedTerm
157 Nb7b562e236e74d249d2e6c6d6a1c88a6 rdf:first sg:person.014030237157.28
158 rdf:rest N33b2a34d89624c1a84b352b980a52ed3
159 Nc258795e32b24689b44882f2feea0da7 rdf:first sg:person.0713154075.07
160 rdf:rest N29c4f65d5e5e4ed699806e81ba35b7b6
161 Nc318d789ad9647fcba4842fefb71e3eb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Female
163 rdf:type schema:DefinedTerm
164 Nd2f77ca67ad84de1b8c47e89bc3a8cf7 schema:name nlm_unique_id
165 schema:value 100969716
166 rdf:type schema:PropertyValue
167 Nd79e8c2edd6741ce8d251635d5b4cb34 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
168 schema:name Coronary Angiography
169 rdf:type schema:DefinedTerm
170 Ndebbbb27f58d4fbc82c136e28ebd43b1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
171 schema:name Cardiac-Gated Imaging Techniques
172 rdf:type schema:DefinedTerm
173 Ne08a49b6f88044bea386761556455800 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
174 schema:name Body Mass Index
175 rdf:type schema:DefinedTerm
176 Nee38d04fff2e4e6a90c434cd95cb69cb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
177 schema:name Chi-Square Distribution
178 rdf:type schema:DefinedTerm
179 Nf582edcd76324273816a5c8dfd94ca52 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
180 schema:name Male
181 rdf:type schema:DefinedTerm
182 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
183 schema:name Physical Sciences
184 rdf:type schema:DefinedTerm
185 anzsrc-for:0299 schema:inDefinedTermSet anzsrc-for:
186 schema:name Other Physical Sciences
187 rdf:type schema:DefinedTerm
188 sg:journal.1025429 schema:issn 1569-5794
189 1573-0743
190 schema:name The International Journal of Cardiovascular Imaging
191 rdf:type schema:Periodical
192 sg:person.01027402475.85 schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
193 schema:familyName Yan
194 schema:givenName Zixu
195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027402475.85
196 rdf:type schema:Person
197 sg:person.01105702465.29 schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
198 schema:familyName Zhang
199 schema:givenName Chuanchen
200 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01105702465.29
201 rdf:type schema:Person
202 sg:person.01143631075.45 schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
203 schema:familyName Yu
204 schema:givenName Wei
205 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01143631075.45
206 rdf:type schema:Person
207 sg:person.014030237157.28 schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
208 schema:familyName Zhang
209 schema:givenName Zhaoqi
210 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014030237157.28
211 rdf:type schema:Person
212 sg:person.0642401716.48 schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
213 schema:familyName Wang
214 schema:givenName Rui
215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0642401716.48
216 rdf:type schema:Person
217 sg:person.0713154075.07 schema:affiliation https://www.grid.ac/institutes/grid.24696.3f
218 schema:familyName Xu
219 schema:givenName Lei
220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0713154075.07
221 rdf:type schema:Person
222 sg:pub.10.1007/s00330-007-0786-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028078235
223 https://doi.org/10.1007/s00330-007-0786-8
224 rdf:type schema:CreativeWork
225 sg:pub.10.1007/s00330-008-0966-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023746890
226 https://doi.org/10.1007/s00330-008-0966-1
227 rdf:type schema:CreativeWork
228 sg:pub.10.1007/s00330-009-1692-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1010453428
229 https://doi.org/10.1007/s00330-009-1692-z
230 rdf:type schema:CreativeWork
231 sg:pub.10.1007/s10554-008-9308-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026084106
232 https://doi.org/10.1007/s10554-008-9308-2
233 rdf:type schema:CreativeWork
234 sg:pub.10.1007/s10554-009-9433-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025927070
235 https://doi.org/10.1007/s10554-009-9433-6
236 rdf:type schema:CreativeWork
237 sg:pub.10.1007/s10554-009-9535-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044896001
238 https://doi.org/10.1007/s10554-009-9535-1
239 rdf:type schema:CreativeWork
240 sg:pub.10.1038/nrcardio.2009.53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028751002
241 https://doi.org/10.1038/nrcardio.2009.53
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1001/jama.2009.54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026937699
244 rdf:type schema:CreativeWork
245 https://doi.org/10.1001/jama.298.3.317 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036295152
246 rdf:type schema:CreativeWork
247 https://doi.org/10.1016/j.acra.2009.09.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031111467
248 rdf:type schema:CreativeWork
249 https://doi.org/10.1016/j.ejrad.2009.07.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026530090
250 rdf:type schema:CreativeWork
251 https://doi.org/10.1016/j.jacc.2005.05.056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002160027
252 rdf:type schema:CreativeWork
253 https://doi.org/10.1016/j.jacc.2009.04.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046273989
254 rdf:type schema:CreativeWork
255 https://doi.org/10.1016/j.jcct.2008.05.146 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027122555
256 rdf:type schema:CreativeWork
257 https://doi.org/10.1016/j.jcct.2008.07.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017395017
258 rdf:type schema:CreativeWork
259 https://doi.org/10.1016/j.jcct.2008.12.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035503600
260 rdf:type schema:CreativeWork
261 https://doi.org/10.1016/j.jcct.2009.05.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027354141
262 rdf:type schema:CreativeWork
263 https://doi.org/10.1016/j.jcmg.2007.11.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026657493
264 rdf:type schema:CreativeWork
265 https://doi.org/10.1016/j.jcmg.2009.02.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030103166
266 rdf:type schema:CreativeWork
267 https://doi.org/10.1093/eurheartj/ehp571 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037926070
268 rdf:type schema:CreativeWork
269 https://doi.org/10.1097/01.rct.0000236422.35761.a1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034755139
270 rdf:type schema:CreativeWork
271 https://doi.org/10.1097/rct.0b013e31815ea873 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005381836
272 rdf:type schema:CreativeWork
273 https://doi.org/10.1097/rli.0b013e31803b93cf schema:sameAs https://app.dimensions.ai/details/publication/pub.1049206841
274 rdf:type schema:CreativeWork
275 https://doi.org/10.1148/radiol.2311030191 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045870566
276 rdf:type schema:CreativeWork
277 https://doi.org/10.1148/radiol.2463070989 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037037103
278 rdf:type schema:CreativeWork
279 https://doi.org/10.1148/radiol.2482072192 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024935051
280 rdf:type schema:CreativeWork
281 https://doi.org/10.1148/radiol.2483072032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032386157
282 rdf:type schema:CreativeWork
283 https://doi.org/10.1148/radiol.2531090065 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040898294
284 rdf:type schema:CreativeWork
285 https://doi.org/10.1161/01.cir.0000048965.56529.c2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036313717
286 rdf:type schema:CreativeWork
287 https://doi.org/10.1161/01.cir.51.4.5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063335684
288 rdf:type schema:CreativeWork
289 https://doi.org/10.1161/circulationaha.105.533471 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032983112
290 rdf:type schema:CreativeWork
291 https://doi.org/10.1161/circulationaha.105.602490 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030836310
292 rdf:type schema:CreativeWork
293 https://doi.org/10.1161/circulationaha.106.634808 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001613860
294 rdf:type schema:CreativeWork
295 https://doi.org/10.1161/circulationaha.109.859280 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053471912
296 rdf:type schema:CreativeWork
297 https://doi.org/10.1259/bjr/66519303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064569910
298 rdf:type schema:CreativeWork
299 https://doi.org/10.2214/ajr.05.0216 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069297467
300 rdf:type schema:CreativeWork
301 https://doi.org/10.2214/ajr.07.3124 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069299006
302 rdf:type schema:CreativeWork
303 https://doi.org/10.2214/ajr.08.1347 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069299651
304 rdf:type schema:CreativeWork
305 https://doi.org/10.2214/ajr.09.3543 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069300529
306 rdf:type schema:CreativeWork
307 https://doi.org/10.2215/cjn.05200709 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037961409
308 rdf:type schema:CreativeWork
309 https://www.grid.ac/institutes/grid.24696.3f schema:alternateName Capital Medical University
310 schema:name Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, 100029, Chaoyang District, Beijing, People’s Republic of China
311 rdf:type schema:Organization
 




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


...