Patient Specific Hemodynamics: Combined 4D Flow-Sensitive MRI and CFD View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011-05-04

AUTHORS

A. F. Stalder , Z. Liu , J. Hennig , J. G. Korvink , K. C. Li , M. Markl

ABSTRACT

Both 4D flow-sensitive MRI and computational fluid dynamics (CFD) have successfully been applied to analyze complex 3D flow patterns in the cardiovascular system. However, both modalities suffer from limitations related to spatiotemporal resolution, measurement errors, and noise (MRI) or incomplete model assumptions and boundary conditions (CFD). The aim of this study was to directly compare the results of 4D flow-sensitive MRI and CFD in a simple model system in vitro and in complex models of the thoracic aorta in vivo. By comparing both modalities within a single framework, discrepancies were observed but the overall patterns were coherent. If adequate methods are used (e.g., patient-specific boundary conditions, fine boundary layer mesh), CFD can compute very accurate flow and vessel wall parameters, such as wall shear stress (WSS). The combination of 4D flow-sensitive MRI and CFD can be used to refine both methodologies, which may help to enhance the assessment and understanding of blood flow in vivo. More... »

PAGES

27-38

Book

TITLE

Computational Biomechanics for Medicine

ISBN

978-1-4419-9618-3
978-1-4419-9619-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4419-9619-0_4

DOI

http://dx.doi.org/10.1007/978-1-4419-9619-0_4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005121552


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/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0915", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Interdisciplinary Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Diagnostic Radiology \u2013 Medical Physics, University Hospital, Freiburg, Germany", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China", 
            "Department of Diagnostic Radiology \u2013 Medical Physics, University Hospital, Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stalder", 
        "givenName": "A. F.", 
        "id": "sg:person.0657702403.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0657702403.26"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Liu", 
        "givenName": "Z.", 
        "type": "Person"
      }, 
      {
        "familyName": "Hennig", 
        "givenName": "J.", 
        "id": "sg:person.014462070357.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014462070357.28"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Korvink", 
        "givenName": "J. G.", 
        "id": "sg:person.0614707320.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0614707320.00"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Li", 
        "givenName": "K. C.", 
        "id": "sg:person.01326527402.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326527402.59"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Markl", 
        "givenName": "M.", 
        "id": "sg:person.01071157260.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01071157260.17"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2011-05-04", 
    "datePublishedReg": "2011-05-04", 
    "description": "Both 4D flow-sensitive MRI and computational fluid dynamics (CFD) have successfully been applied to analyze complex 3D flow patterns in the cardiovascular system. However, both modalities suffer from limitations related to spatiotemporal resolution, measurement errors, and noise (MRI) or incomplete model assumptions and boundary conditions (CFD). The aim of this study was to directly compare the results of 4D flow-sensitive MRI and CFD in a simple model system in vitro and in complex models of the thoracic aorta in vivo. By comparing both modalities within a single framework, discrepancies were observed but the overall patterns were coherent. If adequate methods are used (e.g., patient-specific boundary conditions, fine boundary layer mesh), CFD can compute very accurate flow and vessel wall parameters, such as wall shear stress (WSS). The combination of 4D flow-sensitive MRI and CFD can be used to refine both methodologies, which may help to enhance the assessment and understanding of blood flow in vivo.", 
    "editor": [
      {
        "familyName": "Wittek", 
        "givenName": "Adam", 
        "type": "Person"
      }, 
      {
        "familyName": "Nielsen", 
        "givenName": "Poul M.F.", 
        "type": "Person"
      }, 
      {
        "familyName": "Miller", 
        "givenName": "Karol", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-1-4419-9619-0_4", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-1-4419-9618-3", 
        "978-1-4419-9619-0"
      ], 
      "name": "Computational Biomechanics for Medicine", 
      "type": "Book"
    }, 
    "keywords": [
      "computational fluid dynamics", 
      "wall shear stress", 
      "complex 3D flow patterns", 
      "boundary conditions", 
      "fluid dynamics", 
      "complex models", 
      "accurate flow", 
      "simple model system", 
      "model assumptions", 
      "measurement error", 
      "shear stress", 
      "flow patterns", 
      "wall parameters", 
      "single framework", 
      "vessel wall parameters", 
      "flow-sensitive MRI", 
      "flow", 
      "spatiotemporal resolution", 
      "dynamics", 
      "noise", 
      "assumption", 
      "error", 
      "system", 
      "parameters", 
      "model", 
      "framework", 
      "methodology", 
      "stress", 
      "model system", 
      "adequate method", 
      "conditions", 
      "resolution", 
      "method", 
      "results", 
      "discrepancy", 
      "limitations", 
      "sensitive MRI", 
      "combination", 
      "patterns", 
      "assessment", 
      "study", 
      "understanding", 
      "aim", 
      "MRI", 
      "blood flow", 
      "cardiovascular system", 
      "overall pattern", 
      "modalities", 
      "thoracic aorta", 
      "vivo", 
      "aorta", 
      "vitro"
    ], 
    "name": "Patient Specific Hemodynamics: Combined 4D Flow-Sensitive MRI and CFD", 
    "pagination": "27-38", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1005121552"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-1-4419-9619-0_4"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-1-4419-9619-0_4", 
      "https://app.dimensions.ai/details/publication/pub.1005121552"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-12-01T06:50", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/chapter/chapter_290.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-1-4419-9619-0_4"
  }
]
 

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.10.1007/978-1-4419-9619-0_4'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-1-4419-9619-0_4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-1-4419-9619-0_4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-1-4419-9619-0_4'


 

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

151 TRIPLES      22 PREDICATES      76 URIs      69 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-1-4419-9619-0_4 schema:about anzsrc-for:09
2 anzsrc-for:0915
3 schema:author N1cfdeda2be6a4b809a6f97f9e89c88f1
4 schema:datePublished 2011-05-04
5 schema:datePublishedReg 2011-05-04
6 schema:description Both 4D flow-sensitive MRI and computational fluid dynamics (CFD) have successfully been applied to analyze complex 3D flow patterns in the cardiovascular system. However, both modalities suffer from limitations related to spatiotemporal resolution, measurement errors, and noise (MRI) or incomplete model assumptions and boundary conditions (CFD). The aim of this study was to directly compare the results of 4D flow-sensitive MRI and CFD in a simple model system in vitro and in complex models of the thoracic aorta in vivo. By comparing both modalities within a single framework, discrepancies were observed but the overall patterns were coherent. If adequate methods are used (e.g., patient-specific boundary conditions, fine boundary layer mesh), CFD can compute very accurate flow and vessel wall parameters, such as wall shear stress (WSS). The combination of 4D flow-sensitive MRI and CFD can be used to refine both methodologies, which may help to enhance the assessment and understanding of blood flow in vivo.
7 schema:editor Neb7ca7399239440a953395d4471674a7
8 schema:genre chapter
9 schema:isAccessibleForFree false
10 schema:isPartOf N3608532daa79410b9e421f1a23c33ee8
11 schema:keywords MRI
12 accurate flow
13 adequate method
14 aim
15 aorta
16 assessment
17 assumption
18 blood flow
19 boundary conditions
20 cardiovascular system
21 combination
22 complex 3D flow patterns
23 complex models
24 computational fluid dynamics
25 conditions
26 discrepancy
27 dynamics
28 error
29 flow
30 flow patterns
31 flow-sensitive MRI
32 fluid dynamics
33 framework
34 limitations
35 measurement error
36 method
37 methodology
38 modalities
39 model
40 model assumptions
41 model system
42 noise
43 overall pattern
44 parameters
45 patterns
46 resolution
47 results
48 sensitive MRI
49 shear stress
50 simple model system
51 single framework
52 spatiotemporal resolution
53 stress
54 study
55 system
56 thoracic aorta
57 understanding
58 vessel wall parameters
59 vitro
60 vivo
61 wall parameters
62 wall shear stress
63 schema:name Patient Specific Hemodynamics: Combined 4D Flow-Sensitive MRI and CFD
64 schema:pagination 27-38
65 schema:productId N1cfc957582c648bda57c5c741850779b
66 N6e863354222f49708170837e0bd8403f
67 schema:publisher Ncb2539a11d1348569c96a890f5b25e96
68 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005121552
69 https://doi.org/10.1007/978-1-4419-9619-0_4
70 schema:sdDatePublished 2022-12-01T06:50
71 schema:sdLicense https://scigraph.springernature.com/explorer/license/
72 schema:sdPublisher N58fb850869fb49daa6da97bc1881599c
73 schema:url https://doi.org/10.1007/978-1-4419-9619-0_4
74 sgo:license sg:explorer/license/
75 sgo:sdDataset chapters
76 rdf:type schema:Chapter
77 N0a840d04b97d4ca79d1ee9ace2f8e885 rdf:first N70688ef5114f49219fe57657062489eb
78 rdf:rest N484345fa2f2a43e88743ae70d3b4c92e
79 N1cfc957582c648bda57c5c741850779b schema:name dimensions_id
80 schema:value pub.1005121552
81 rdf:type schema:PropertyValue
82 N1cfdeda2be6a4b809a6f97f9e89c88f1 rdf:first sg:person.0657702403.26
83 rdf:rest N0a840d04b97d4ca79d1ee9ace2f8e885
84 N3608532daa79410b9e421f1a23c33ee8 schema:isbn 978-1-4419-9618-3
85 978-1-4419-9619-0
86 schema:name Computational Biomechanics for Medicine
87 rdf:type schema:Book
88 N484345fa2f2a43e88743ae70d3b4c92e rdf:first sg:person.014462070357.28
89 rdf:rest Nbc81f3e88eb34a5dad6e8018a9f21ee9
90 N4cb940f127704d6ebb4bd4801d12c723 schema:familyName Wittek
91 schema:givenName Adam
92 rdf:type schema:Person
93 N58fb850869fb49daa6da97bc1881599c schema:name Springer Nature - SN SciGraph project
94 rdf:type schema:Organization
95 N6a0612a4154e4cc7837daeb257189678 rdf:first N70e534163faa46408c876d23363c9eab
96 rdf:rest N99d157ccb22f4315aa1e0fab4153a096
97 N6e863354222f49708170837e0bd8403f schema:name doi
98 schema:value 10.1007/978-1-4419-9619-0_4
99 rdf:type schema:PropertyValue
100 N70688ef5114f49219fe57657062489eb schema:familyName Liu
101 schema:givenName Z.
102 rdf:type schema:Person
103 N70e534163faa46408c876d23363c9eab schema:familyName Nielsen
104 schema:givenName Poul M.F.
105 rdf:type schema:Person
106 N90fda38d724e41838308d69943455396 schema:familyName Miller
107 schema:givenName Karol
108 rdf:type schema:Person
109 N99d157ccb22f4315aa1e0fab4153a096 rdf:first N90fda38d724e41838308d69943455396
110 rdf:rest rdf:nil
111 Nabc6677897d44b2fbdb68c6617e453b3 rdf:first sg:person.01326527402.59
112 rdf:rest Nd965a3abdbbc4d7d8bb08bcbd4c19616
113 Nbc81f3e88eb34a5dad6e8018a9f21ee9 rdf:first sg:person.0614707320.00
114 rdf:rest Nabc6677897d44b2fbdb68c6617e453b3
115 Ncb2539a11d1348569c96a890f5b25e96 schema:name Springer Nature
116 rdf:type schema:Organisation
117 Nd965a3abdbbc4d7d8bb08bcbd4c19616 rdf:first sg:person.01071157260.17
118 rdf:rest rdf:nil
119 Neb7ca7399239440a953395d4471674a7 rdf:first N4cb940f127704d6ebb4bd4801d12c723
120 rdf:rest N6a0612a4154e4cc7837daeb257189678
121 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
122 schema:name Engineering
123 rdf:type schema:DefinedTerm
124 anzsrc-for:0915 schema:inDefinedTermSet anzsrc-for:
125 schema:name Interdisciplinary Engineering
126 rdf:type schema:DefinedTerm
127 sg:person.01071157260.17 schema:familyName Markl
128 schema:givenName M.
129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01071157260.17
130 rdf:type schema:Person
131 sg:person.01326527402.59 schema:familyName Li
132 schema:givenName K. C.
133 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01326527402.59
134 rdf:type schema:Person
135 sg:person.014462070357.28 schema:familyName Hennig
136 schema:givenName J.
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014462070357.28
138 rdf:type schema:Person
139 sg:person.0614707320.00 schema:familyName Korvink
140 schema:givenName J. G.
141 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0614707320.00
142 rdf:type schema:Person
143 sg:person.0657702403.26 schema:affiliation grid-institutes:None
144 schema:familyName Stalder
145 schema:givenName A. F.
146 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0657702403.26
147 rdf:type schema:Person
148 grid-institutes:None schema:alternateName Department of Diagnostic Radiology – Medical Physics, University Hospital, Freiburg, Germany
149 schema:name Department of Diagnostic Radiology – Medical Physics, University Hospital, Freiburg, Germany
150 Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
151 rdf:type schema:Organization
 




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


...