On-site evaluation of CT-based fractional flow reserve using simple boundary conditions for computational fluid dynamics View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-10-18

AUTHORS

Yusuke Yoshikawa, Masahiko Nakamoto, Masanori Nakamura, Takeharu Hoshi, Erika Yamamoto, Shunsuke Imai, Yoshiaki Kawase, Munenori Okubo, Hiroki Shiomi, Takeshi Kondo, Hitoshi Matsuo, Takeshi Kimura, Naritatsu Saito

ABSTRACT

Fractional flow reserve (FFR) is an established method for diagnosing physiological coronary artery stenosis. A method for computing FFR using coronary computed tomography (CT) images was recently developed. However, its calculation requires off-site supercomputer analysis. Here, we report the preliminary result of a method using simple estimation of boundary conditions. The lumen boundaries of the coronary arteries were semi-automatically delineated using full width at half maximum of CT number profiles. The computational fluid dynamics (CFD) of the blood flow was performed using the boundary conditions of a fixed pressure at the coronary ostium and flow rates at each outlet. The total inflow at the coronary ostium was estimated based on the uniform wall shear stress hypothesis and corrected using a hyperemic multiplier to gain a hyperemic flow rate. The flow distribution from a parent vessel to the downstream daughter vessels was determined according to Murray’s law. FFR estimated by CFD was calculated as FFRCFD = Pd/Pa. We collected patients who underwent coronary CT and coronary angiography followed by invasively measured FFR and compared FFRCFD with FFR. Sensitivity, specificity, and correlations were assessed. A total of 48 patients and 72 arteries were assessed. The correlation coefficient of FFRCFD with FFR was 0.56. The cut-off value was ≤ 0.80, sensitivity was 59.1%, and specificity was 94.0%. CFD-based FFR using simple boundary conditions for on-site clinical computation provided FFRCFD values that were moderately correlated with invasively measured FFR. More... »

PAGES

337-346

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10554-019-01709-3

DOI

http://dx.doi.org/10.1007/s10554-019-01709-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1121923432

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/31628575


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Cardiorespiratory Medicine and Haematology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computed Tomography Angiography", 
        "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": "Coronary Vessels", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fractional Flow Reserve, Myocardial", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Hydrodynamics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Cardiovascular", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Patient-Specific Modeling", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Predictive Value of Tests", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Retrospective Studies", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan", 
          "id": "http://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yoshikawa", 
        "givenName": "Yusuke", 
        "id": "sg:person.01305534723.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01305534723.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "EBM Corporation, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "EBM Corporation, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nakamoto", 
        "givenName": "Masahiko", 
        "id": "sg:person.013644011463.13", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013644011463.13"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Biomechanics Laboratory, Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Japan", 
          "id": "http://www.grid.ac/institutes/grid.47716.33", 
          "name": [
            "Biomechanics Laboratory, Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nakamura", 
        "givenName": "Masanori", 
        "id": "sg:person.016250035251.11", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016250035251.11"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "EBM Corporation, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "EBM Corporation, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hoshi", 
        "givenName": "Takeharu", 
        "id": "sg:person.015236752463.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015236752463.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan", 
          "id": "http://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yamamoto", 
        "givenName": "Erika", 
        "id": "sg:person.07634657005.91", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07634657005.91"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.511555.0", 
          "name": [
            "Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Imai", 
        "givenName": "Shunsuke", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.511555.0", 
          "name": [
            "Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kawase", 
        "givenName": "Yoshiaki", 
        "id": "sg:person.0623224142.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0623224142.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.511555.0", 
          "name": [
            "Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Okubo", 
        "givenName": "Munenori", 
        "id": "sg:person.01246573553.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01246573553.35"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan", 
          "id": "http://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shiomi", 
        "givenName": "Hiroki", 
        "id": "sg:person.01272152120.47", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01272152120.47"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.511555.0", 
          "name": [
            "Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kondo", 
        "givenName": "Takeshi", 
        "id": "sg:person.0702243644.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0702243644.40"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan", 
          "id": "http://www.grid.ac/institutes/grid.511555.0", 
          "name": [
            "Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Matsuo", 
        "givenName": "Hitoshi", 
        "id": "sg:person.01047645033.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01047645033.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan", 
          "id": "http://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kimura", 
        "givenName": "Takeshi", 
        "id": "sg:person.012173163322.36", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012173163322.36"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan", 
          "id": "http://www.grid.ac/institutes/grid.258799.8", 
          "name": [
            "Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Saito", 
        "givenName": "Naritatsu", 
        "id": "sg:person.01354667527.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354667527.07"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s10237-016-0773-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033964874", 
          "https://doi.org/10.1007/s10237-016-0773-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12938-017-0330-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084826356", 
          "https://doi.org/10.1186/s12938-017-0330-2"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-10-18", 
    "datePublishedReg": "2019-10-18", 
    "description": "Fractional flow reserve (FFR) is an established method for diagnosing physiological coronary artery stenosis. A method for computing FFR using coronary computed tomography (CT) images was recently developed. However, its calculation requires off-site supercomputer analysis. Here, we report the preliminary result of a method using simple estimation of boundary conditions. The lumen boundaries of the coronary arteries were semi-automatically delineated using full width at half maximum of CT number profiles. The computational fluid dynamics (CFD) of the blood flow was performed using the boundary conditions of a fixed pressure at the coronary ostium and flow rates at each outlet. The total inflow at the coronary ostium was estimated based on the uniform wall shear stress hypothesis and corrected using a hyperemic multiplier to gain a hyperemic flow rate. The flow distribution from a parent vessel to the downstream daughter vessels was determined according to Murray\u2019s law. FFR estimated by CFD was calculated as FFRCFD = Pd/Pa. We collected patients who underwent coronary CT and coronary angiography followed by invasively measured FFR and compared FFRCFD with FFR. Sensitivity, specificity, and correlations were assessed. A total of 48 patients and 72 arteries were assessed. The correlation coefficient of FFRCFD with FFR was 0.56. The cut-off value was \u2264 0.80, sensitivity was 59.1%, and specificity was 94.0%. CFD-based FFR using simple boundary conditions for on-site clinical computation provided FFRCFD values that were moderately correlated with invasively measured FFR.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s10554-019-01709-3", 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.8438883", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1025429", 
        "issn": [
          "1569-5794", 
          "1573-0743"
        ], 
        "name": "The International Journal of Cardiovascular Imaging", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "36"
      }
    ], 
    "keywords": [
      "computational fluid dynamics", 
      "simple boundary conditions", 
      "fluid dynamics", 
      "boundary conditions", 
      "flow rate", 
      "flow distribution", 
      "half maximum", 
      "full width", 
      "total inflow", 
      "Murray's law", 
      "simple estimation", 
      "number profiles", 
      "daughter vessels", 
      "CT number profile", 
      "lumen boundaries", 
      "conditions", 
      "method", 
      "flow", 
      "parent vessel", 
      "preliminary results", 
      "outlet", 
      "width", 
      "coefficient", 
      "boundaries", 
      "dynamics", 
      "PA", 
      "estimation", 
      "multipliers", 
      "law", 
      "pressure", 
      "tomography images", 
      "inflow", 
      "values", 
      "site evaluation", 
      "calculations", 
      "rate", 
      "vessels", 
      "sensitivity", 
      "computation", 
      "distribution", 
      "maximum", 
      "images", 
      "results", 
      "stress hypothesis", 
      "reserves", 
      "profile", 
      "correlation coefficient", 
      "analysis", 
      "evaluation", 
      "correlation", 
      "Pd/Pa", 
      "fractional flow reserve", 
      "blood flow", 
      "CT", 
      "artery", 
      "ostium", 
      "flow reserve", 
      "coronary ostium", 
      "hypothesis", 
      "coronary artery", 
      "specificity", 
      "total", 
      "stenosis", 
      "angiography", 
      "coronary artery stenosis", 
      "coronary CT", 
      "artery stenosis", 
      "patients", 
      "coronary angiography"
    ], 
    "name": "On-site evaluation of CT-based fractional flow reserve using simple boundary conditions for computational fluid dynamics", 
    "pagination": "337-346", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1121923432"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10554-019-01709-3"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "31628575"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10554-019-01709-3", 
      "https://app.dimensions.ai/details/publication/pub.1121923432"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-09-02T16:03", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_802.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s10554-019-01709-3"
  }
]
 

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/s10554-019-01709-3'

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/s10554-019-01709-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10554-019-01709-3'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10554-019-01709-3'


 

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

296 TRIPLES      21 PREDICATES      112 URIs      102 LITERALS      23 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10554-019-01709-3 schema:about N14a5c2f9b04e48779158b0065b211d36
2 N302f41db40ae4de09d845892360b83e1
3 N304941bb6ac74309ad40ad49da38e1ca
4 N41f06ce1e3894d54b93d74aa9b55798e
5 N44edfcb0b2a447eda2273bf18e664206
6 N47ddb827219e4d88b85f5ef94d42de31
7 N50d19f6cc25f4433bc149e5f214eefcb
8 N5b4bd3f1e2c846038ec05d6883260f8d
9 N651f62c09d3a4457ab84b76d5cea1ba2
10 N6c9574e64d784f278ee370b26f88fa14
11 N8ae6ee825c3d4c738a072a0649aaed6d
12 N90455497f698490fa8178fdd28d0ace5
13 N938c3aa60760475d8514323e718c961f
14 N9f79fa578cb84920aae087d1e55b85ab
15 Na6e38df12b8f4993b289f995a01c2d70
16 Ncb63fe2f05ce4b10a94de38beebda866
17 anzsrc-for:11
18 anzsrc-for:1102
19 schema:author N92a7ab2e29ce41fd9ddc7ad5592ff20c
20 schema:citation sg:pub.10.1007/s10237-016-0773-6
21 sg:pub.10.1186/s12938-017-0330-2
22 schema:datePublished 2019-10-18
23 schema:datePublishedReg 2019-10-18
24 schema:description Fractional flow reserve (FFR) is an established method for diagnosing physiological coronary artery stenosis. A method for computing FFR using coronary computed tomography (CT) images was recently developed. However, its calculation requires off-site supercomputer analysis. Here, we report the preliminary result of a method using simple estimation of boundary conditions. The lumen boundaries of the coronary arteries were semi-automatically delineated using full width at half maximum of CT number profiles. The computational fluid dynamics (CFD) of the blood flow was performed using the boundary conditions of a fixed pressure at the coronary ostium and flow rates at each outlet. The total inflow at the coronary ostium was estimated based on the uniform wall shear stress hypothesis and corrected using a hyperemic multiplier to gain a hyperemic flow rate. The flow distribution from a parent vessel to the downstream daughter vessels was determined according to Murray’s law. FFR estimated by CFD was calculated as FFRCFD = Pd/Pa. We collected patients who underwent coronary CT and coronary angiography followed by invasively measured FFR and compared FFRCFD with FFR. Sensitivity, specificity, and correlations were assessed. A total of 48 patients and 72 arteries were assessed. The correlation coefficient of FFRCFD with FFR was 0.56. The cut-off value was ≤ 0.80, sensitivity was 59.1%, and specificity was 94.0%. CFD-based FFR using simple boundary conditions for on-site clinical computation provided FFRCFD values that were moderately correlated with invasively measured FFR.
25 schema:genre article
26 schema:isAccessibleForFree false
27 schema:isPartOf N20ee25a9a6624e17964a4b8db89f3815
28 N5110f12e3c16491dac611286ebf1ca0c
29 sg:journal.1025429
30 schema:keywords CT
31 CT number profile
32 Murray's law
33 PA
34 Pd/Pa
35 analysis
36 angiography
37 artery
38 artery stenosis
39 blood flow
40 boundaries
41 boundary conditions
42 calculations
43 coefficient
44 computation
45 computational fluid dynamics
46 conditions
47 coronary CT
48 coronary angiography
49 coronary artery
50 coronary artery stenosis
51 coronary ostium
52 correlation
53 correlation coefficient
54 daughter vessels
55 distribution
56 dynamics
57 estimation
58 evaluation
59 flow
60 flow distribution
61 flow rate
62 flow reserve
63 fluid dynamics
64 fractional flow reserve
65 full width
66 half maximum
67 hypothesis
68 images
69 inflow
70 law
71 lumen boundaries
72 maximum
73 method
74 multipliers
75 number profiles
76 ostium
77 outlet
78 parent vessel
79 patients
80 preliminary results
81 pressure
82 profile
83 rate
84 reserves
85 results
86 sensitivity
87 simple boundary conditions
88 simple estimation
89 site evaluation
90 specificity
91 stenosis
92 stress hypothesis
93 tomography images
94 total
95 total inflow
96 values
97 vessels
98 width
99 schema:name On-site evaluation of CT-based fractional flow reserve using simple boundary conditions for computational fluid dynamics
100 schema:pagination 337-346
101 schema:productId N459c8b96e9b846e5979d3bc62448a92f
102 N939f49ebac014969b6aaedd62388cef3
103 Nf8d97bf393ce439d8cf320fc0f231ffb
104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1121923432
105 https://doi.org/10.1007/s10554-019-01709-3
106 schema:sdDatePublished 2022-09-02T16:03
107 schema:sdLicense https://scigraph.springernature.com/explorer/license/
108 schema:sdPublisher N08830d532c7c47eeba5adb246284cdc6
109 schema:url https://doi.org/10.1007/s10554-019-01709-3
110 sgo:license sg:explorer/license/
111 sgo:sdDataset articles
112 rdf:type schema:ScholarlyArticle
113 N08830d532c7c47eeba5adb246284cdc6 schema:name Springer Nature - SN SciGraph project
114 rdf:type schema:Organization
115 N0b197d620fb749e29ec6ed048a11b518 rdf:first Na6b85a936c5e44a6a3853cd7e9f16b1a
116 rdf:rest Nf2030fd2862d46cba101378fc4fb398f
117 N14a5c2f9b04e48779158b0065b211d36 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Female
119 rdf:type schema:DefinedTerm
120 N16f07ae573ba48548a823eb9563354ec rdf:first sg:person.01246573553.35
121 rdf:rest N69fa23809edc417bb057ed6f4d30a8dc
122 N20ee25a9a6624e17964a4b8db89f3815 schema:volumeNumber 36
123 rdf:type schema:PublicationVolume
124 N302f41db40ae4de09d845892360b83e1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Middle Aged
126 rdf:type schema:DefinedTerm
127 N304941bb6ac74309ad40ad49da38e1ca schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Reproducibility of Results
129 rdf:type schema:DefinedTerm
130 N41f06ce1e3894d54b93d74aa9b55798e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Fractional Flow Reserve, Myocardial
132 rdf:type schema:DefinedTerm
133 N44edfcb0b2a447eda2273bf18e664206 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Coronary Vessels
135 rdf:type schema:DefinedTerm
136 N459c8b96e9b846e5979d3bc62448a92f schema:name pubmed_id
137 schema:value 31628575
138 rdf:type schema:PropertyValue
139 N47ddb827219e4d88b85f5ef94d42de31 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
140 schema:name Retrospective Studies
141 rdf:type schema:DefinedTerm
142 N4e4b965cba6f484a804be77431ae0d02 rdf:first sg:person.013644011463.13
143 rdf:rest Nb0a870bd22ff4ee78754ea1df12c247a
144 N50d19f6cc25f4433bc149e5f214eefcb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
145 schema:name Models, Cardiovascular
146 rdf:type schema:DefinedTerm
147 N5110f12e3c16491dac611286ebf1ca0c schema:issueNumber 2
148 rdf:type schema:PublicationIssue
149 N5b4bd3f1e2c846038ec05d6883260f8d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
150 schema:name Hydrodynamics
151 rdf:type schema:DefinedTerm
152 N651f62c09d3a4457ab84b76d5cea1ba2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
153 schema:name Aged
154 rdf:type schema:DefinedTerm
155 N69fa23809edc417bb057ed6f4d30a8dc rdf:first sg:person.01272152120.47
156 rdf:rest Nb3bb0ae362644ec19af03d4712957dd0
157 N6c9574e64d784f278ee370b26f88fa14 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
158 schema:name Predictive Value of Tests
159 rdf:type schema:DefinedTerm
160 N8a1d175750754e7f8b269beb293e403b rdf:first sg:person.012173163322.36
161 rdf:rest N928cf15d645d455db140683a56ab3e96
162 N8ae6ee825c3d4c738a072a0649aaed6d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
163 schema:name Coronary Angiography
164 rdf:type schema:DefinedTerm
165 N90455497f698490fa8178fdd28d0ace5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
166 schema:name Male
167 rdf:type schema:DefinedTerm
168 N928cf15d645d455db140683a56ab3e96 rdf:first sg:person.01354667527.07
169 rdf:rest rdf:nil
170 N92a7ab2e29ce41fd9ddc7ad5592ff20c rdf:first sg:person.01305534723.52
171 rdf:rest N4e4b965cba6f484a804be77431ae0d02
172 N938c3aa60760475d8514323e718c961f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
173 schema:name Humans
174 rdf:type schema:DefinedTerm
175 N939f49ebac014969b6aaedd62388cef3 schema:name doi
176 schema:value 10.1007/s10554-019-01709-3
177 rdf:type schema:PropertyValue
178 N9f79fa578cb84920aae087d1e55b85ab schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
179 schema:name Computed Tomography Angiography
180 rdf:type schema:DefinedTerm
181 Na6b85a936c5e44a6a3853cd7e9f16b1a schema:affiliation grid-institutes:grid.511555.0
182 schema:familyName Imai
183 schema:givenName Shunsuke
184 rdf:type schema:Person
185 Na6e38df12b8f4993b289f995a01c2d70 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
186 schema:name Patient-Specific Modeling
187 rdf:type schema:DefinedTerm
188 Nb0a870bd22ff4ee78754ea1df12c247a rdf:first sg:person.016250035251.11
189 rdf:rest Nd57d73806f8d4074ad90d94d509c7cc5
190 Nb3bb0ae362644ec19af03d4712957dd0 rdf:first sg:person.0702243644.40
191 rdf:rest Nf8a155280c3841beaac0ee3a52451235
192 Nc6ea869ae65a4081925cdf779eff0e50 rdf:first sg:person.07634657005.91
193 rdf:rest N0b197d620fb749e29ec6ed048a11b518
194 Ncb63fe2f05ce4b10a94de38beebda866 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
195 schema:name Coronary Artery Disease
196 rdf:type schema:DefinedTerm
197 Nd57d73806f8d4074ad90d94d509c7cc5 rdf:first sg:person.015236752463.29
198 rdf:rest Nc6ea869ae65a4081925cdf779eff0e50
199 Nf2030fd2862d46cba101378fc4fb398f rdf:first sg:person.0623224142.50
200 rdf:rest N16f07ae573ba48548a823eb9563354ec
201 Nf8a155280c3841beaac0ee3a52451235 rdf:first sg:person.01047645033.52
202 rdf:rest N8a1d175750754e7f8b269beb293e403b
203 Nf8d97bf393ce439d8cf320fc0f231ffb schema:name dimensions_id
204 schema:value pub.1121923432
205 rdf:type schema:PropertyValue
206 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
207 schema:name Medical and Health Sciences
208 rdf:type schema:DefinedTerm
209 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
210 schema:name Cardiorespiratory Medicine and Haematology
211 rdf:type schema:DefinedTerm
212 sg:grant.8438883 http://pending.schema.org/fundedItem sg:pub.10.1007/s10554-019-01709-3
213 rdf:type schema:MonetaryGrant
214 sg:journal.1025429 schema:issn 1569-5794
215 1573-0743
216 schema:name The International Journal of Cardiovascular Imaging
217 schema:publisher Springer Nature
218 rdf:type schema:Periodical
219 sg:person.01047645033.52 schema:affiliation grid-institutes:grid.511555.0
220 schema:familyName Matsuo
221 schema:givenName Hitoshi
222 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01047645033.52
223 rdf:type schema:Person
224 sg:person.012173163322.36 schema:affiliation grid-institutes:grid.258799.8
225 schema:familyName Kimura
226 schema:givenName Takeshi
227 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012173163322.36
228 rdf:type schema:Person
229 sg:person.01246573553.35 schema:affiliation grid-institutes:grid.511555.0
230 schema:familyName Okubo
231 schema:givenName Munenori
232 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01246573553.35
233 rdf:type schema:Person
234 sg:person.01272152120.47 schema:affiliation grid-institutes:grid.258799.8
235 schema:familyName Shiomi
236 schema:givenName Hiroki
237 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01272152120.47
238 rdf:type schema:Person
239 sg:person.01305534723.52 schema:affiliation grid-institutes:grid.258799.8
240 schema:familyName Yoshikawa
241 schema:givenName Yusuke
242 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01305534723.52
243 rdf:type schema:Person
244 sg:person.01354667527.07 schema:affiliation grid-institutes:grid.258799.8
245 schema:familyName Saito
246 schema:givenName Naritatsu
247 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354667527.07
248 rdf:type schema:Person
249 sg:person.013644011463.13 schema:affiliation grid-institutes:None
250 schema:familyName Nakamoto
251 schema:givenName Masahiko
252 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013644011463.13
253 rdf:type schema:Person
254 sg:person.015236752463.29 schema:affiliation grid-institutes:None
255 schema:familyName Hoshi
256 schema:givenName Takeharu
257 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015236752463.29
258 rdf:type schema:Person
259 sg:person.016250035251.11 schema:affiliation grid-institutes:grid.47716.33
260 schema:familyName Nakamura
261 schema:givenName Masanori
262 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016250035251.11
263 rdf:type schema:Person
264 sg:person.0623224142.50 schema:affiliation grid-institutes:grid.511555.0
265 schema:familyName Kawase
266 schema:givenName Yoshiaki
267 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0623224142.50
268 rdf:type schema:Person
269 sg:person.0702243644.40 schema:affiliation grid-institutes:grid.511555.0
270 schema:familyName Kondo
271 schema:givenName Takeshi
272 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0702243644.40
273 rdf:type schema:Person
274 sg:person.07634657005.91 schema:affiliation grid-institutes:grid.258799.8
275 schema:familyName Yamamoto
276 schema:givenName Erika
277 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07634657005.91
278 rdf:type schema:Person
279 sg:pub.10.1007/s10237-016-0773-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033964874
280 https://doi.org/10.1007/s10237-016-0773-6
281 rdf:type schema:CreativeWork
282 sg:pub.10.1186/s12938-017-0330-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084826356
283 https://doi.org/10.1186/s12938-017-0330-2
284 rdf:type schema:CreativeWork
285 grid-institutes:None schema:alternateName EBM Corporation, Tokyo, Japan
286 schema:name EBM Corporation, Tokyo, Japan
287 rdf:type schema:Organization
288 grid-institutes:grid.258799.8 schema:alternateName Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan
289 schema:name Department of Cardiovascular Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-cho, Sakyo-ku, 606-8507, Kyoto, Japan
290 rdf:type schema:Organization
291 grid-institutes:grid.47716.33 schema:alternateName Biomechanics Laboratory, Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Japan
292 schema:name Biomechanics Laboratory, Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Japan
293 rdf:type schema:Organization
294 grid-institutes:grid.511555.0 schema:alternateName Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan
295 schema:name Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan
296 rdf:type schema:Organization
 




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


...