Estimation of Regional Electrical Properties of the Heart from 12-Lead ECG and Images View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015-01-01

AUTHORS

Philipp Seegerer , Tommaso Mansi , Marie-Pierre Jolly , Dominik Neumann , Bogdan Georgescu , Ali Kamen , Elham Kayvanpour , Ali Amr , Farbod Sedaghat-Hamedani , Jan Haas , Hugo Katus , Benjamin Meder , Dorin Comaniciu

ABSTRACT

Computational models of cardiac electrophysiology are being investigated for improved patient selection and planning of therapies like cardiac resynchronization therapy (CRT). However, their clinical applicability is limited unless their parameters are fitted to the physiology of an individual patient. In this paper, a method that estimates spatially-varying electrical diffusivities from routine ECG data and dynamic cardiac images is presented. Contrary to current methods based on invasive electrophysiology studies or body surface potential mapping, our approach relies on widely available 12-lead ECG and motion information obtained from clinical images. First, a map of mechanical activation time is derived from a cardiac strain map. Then, regional electrical diffusivities are personalized such that the computed cardiac depolarization matches both the mechanical activation map and measured ECG features. The fit between measured and computed electrocardiography data after model personalization is evaluated on 14 dilated cardiomyopathy patients, exhibiting low mean errors in terms of the diagnostic ECG features QRS duration (0.1 ms) and electrical axis (10.6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}). The proposed regional approach outperforms global personalization when 12-lead ECG is the only electrophysiology data available. Furthermore, promising results of a preliminary CRT study on one patient demonstrate the predictive power of the personalized model. More... »

PAGES

204-212

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-14678-2_21

DOI

http://dx.doi.org/10.1007/978-3-319-14678-2_21

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050554111


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Pattern Recognition Lab, FAU Erlangen-N\u00fcrnberg, Erlangen, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5330.5", 
          "name": [
            "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA", 
            "Pattern Recognition Lab, FAU Erlangen-N\u00fcrnberg, Erlangen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Seegerer", 
        "givenName": "Philipp", 
        "id": "sg:person.016076162221.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016076162221.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mansi", 
        "givenName": "Tommaso", 
        "id": "sg:person.01217474726.73", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01217474726.73"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jolly", 
        "givenName": "Marie-Pierre", 
        "id": "sg:person.0614041027.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0614041027.22"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pattern Recognition Lab, FAU Erlangen-N\u00fcrnberg, Erlangen, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5330.5", 
          "name": [
            "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA", 
            "Pattern Recognition Lab, FAU Erlangen-N\u00fcrnberg, Erlangen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Neumann", 
        "givenName": "Dominik", 
        "id": "sg:person.01054566020.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054566020.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Georgescu", 
        "givenName": "Bogdan", 
        "id": "sg:person.0703547214.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0703547214.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kamen", 
        "givenName": "Ali", 
        "id": "sg:person.0656777564.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0656777564.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5253.1", 
          "name": [
            "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kayvanpour", 
        "givenName": "Elham", 
        "id": "sg:person.01201613000.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01201613000.02"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5253.1", 
          "name": [
            "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Amr", 
        "givenName": "Ali", 
        "id": "sg:person.0644724502.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0644724502.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5253.1", 
          "name": [
            "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sedaghat-Hamedani", 
        "givenName": "Farbod", 
        "id": "sg:person.01247726200.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247726200.41"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5253.1", 
          "name": [
            "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Haas", 
        "givenName": "Jan", 
        "id": "sg:person.01173725567.20", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01173725567.20"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5253.1", 
          "name": [
            "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Katus", 
        "givenName": "Hugo", 
        "id": "sg:person.011260235657.38", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011260235657.38"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5253.1", 
          "name": [
            "Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Meder", 
        "givenName": "Benjamin", 
        "id": "sg:person.01027273360.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027273360.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Comaniciu", 
        "givenName": "Dorin", 
        "id": "sg:person.01066111014.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066111014.77"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2015-01-01", 
    "datePublishedReg": "2015-01-01", 
    "description": "Computational models of cardiac electrophysiology are being investigated for improved patient selection and planning of therapies like cardiac resynchronization therapy (CRT). However, their clinical applicability is limited unless their parameters are fitted to the physiology of an individual patient. In this paper, a method that estimates spatially-varying electrical diffusivities from routine ECG data and dynamic cardiac images is presented. Contrary to current methods based on invasive electrophysiology studies or body surface potential mapping, our approach relies on widely available 12-lead\u00a0ECG and motion information obtained from clinical images. First, a map of mechanical activation time is derived from a cardiac strain map. Then, regional electrical diffusivities are personalized such that the computed cardiac depolarization matches both the mechanical activation map and measured ECG features. The fit between measured and computed electrocardiography data after model personalization is evaluated on 14 dilated cardiomyopathy patients, exhibiting low mean errors in terms of the diagnostic ECG features QRS\u00a0duration (0.1\u00a0ms) and electrical axis (10.6\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$^{\\circ }$$\\end{document}). The proposed regional approach outperforms global personalization when 12-lead ECG is the only electrophysiology data available. Furthermore, promising results of a preliminary CRT study on one patient demonstrate the predictive power of the personalized model.", 
    "editor": [
      {
        "familyName": "Camara", 
        "givenName": "Oscar", 
        "type": "Person"
      }, 
      {
        "familyName": "Mansi", 
        "givenName": "Tommaso", 
        "type": "Person"
      }, 
      {
        "familyName": "Pop", 
        "givenName": "Mihaela", 
        "type": "Person"
      }, 
      {
        "familyName": "Rhode", 
        "givenName": "Kawal", 
        "type": "Person"
      }, 
      {
        "familyName": "Sermesant", 
        "givenName": "Maxime", 
        "type": "Person"
      }, 
      {
        "familyName": "Young", 
        "givenName": "Alistair", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-14678-2_21", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-14677-5", 
        "978-3-319-14678-2"
      ], 
      "name": "Statistical Atlases and Computational Models of the Heart - Imaging and Modelling Challenges", 
      "type": "Book"
    }, 
    "keywords": [
      "cardiac resynchronization therapy", 
      "invasive electrophysiology study", 
      "body surface potential mapping", 
      "planning of therapy", 
      "Regional electrical properties", 
      "resynchronization therapy", 
      "patient selection", 
      "cardiomyopathy patients", 
      "individual patients", 
      "electrophysiology study", 
      "dynamic cardiac images", 
      "patient demonstrate", 
      "CRT studies", 
      "electrical diffusivity", 
      "surface potential mapping", 
      "diagnostic ECG", 
      "clinical applicability", 
      "motion information", 
      "ECG", 
      "model personalization", 
      "patients", 
      "cardiac electrophysiology", 
      "therapy", 
      "electrical axis", 
      "cardiac images", 
      "personalized model", 
      "ECG data", 
      "activation maps", 
      "ECG features", 
      "electrophysiology data", 
      "lowest mean error", 
      "electrocardiography data", 
      "clinical images", 
      "personalization", 
      "computational model", 
      "images", 
      "promising results", 
      "QRS", 
      "activation time", 
      "electrophysiology", 
      "mean error", 
      "heart", 
      "study", 
      "duration", 
      "potential mapping", 
      "maps", 
      "current methods", 
      "predictive power", 
      "data", 
      "physiology", 
      "information", 
      "planning", 
      "estimation", 
      "method", 
      "error", 
      "model", 
      "match", 
      "mapping", 
      "features", 
      "applicability", 
      "mechanical activation time", 
      "selection", 
      "time", 
      "axis", 
      "demonstrate", 
      "results", 
      "approach", 
      "power", 
      "terms", 
      "regional approach", 
      "parameters", 
      "strain maps", 
      "fit", 
      "properties", 
      "electrical properties", 
      "diffusivity", 
      "paper", 
      "routine ECG data", 
      "cardiac strain map", 
      "regional electrical diffusivities", 
      "cardiac depolarization matches", 
      "depolarization matches", 
      "mechanical activation map", 
      "global personalization", 
      "only electrophysiology data", 
      "preliminary CRT study"
    ], 
    "name": "Estimation of Regional Electrical Properties of the Heart from 12-Lead ECG and Images", 
    "pagination": "204-212", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1050554111"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-14678-2_21"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-14678-2_21", 
      "https://app.dimensions.ai/details/publication/pub.1050554111"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:24", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/chapter/chapter_438.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-14678-2_21"
  }
]
 

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.10.1007/978-3-319-14678-2_21'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-14678-2_21'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-14678-2_21'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-14678-2_21'


 

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

270 TRIPLES      23 PREDICATES      113 URIs      104 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-14678-2_21 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 anzsrc-for:11
4 anzsrc-for:1102
5 schema:author N7fa2476767fd422885f74a801bd5fdc2
6 schema:datePublished 2015-01-01
7 schema:datePublishedReg 2015-01-01
8 schema:description Computational models of cardiac electrophysiology are being investigated for improved patient selection and planning of therapies like cardiac resynchronization therapy (CRT). However, their clinical applicability is limited unless their parameters are fitted to the physiology of an individual patient. In this paper, a method that estimates spatially-varying electrical diffusivities from routine ECG data and dynamic cardiac images is presented. Contrary to current methods based on invasive electrophysiology studies or body surface potential mapping, our approach relies on widely available 12-lead ECG and motion information obtained from clinical images. First, a map of mechanical activation time is derived from a cardiac strain map. Then, regional electrical diffusivities are personalized such that the computed cardiac depolarization matches both the mechanical activation map and measured ECG features. The fit between measured and computed electrocardiography data after model personalization is evaluated on 14 dilated cardiomyopathy patients, exhibiting low mean errors in terms of the diagnostic ECG features QRS duration (0.1 ms) and electrical axis (10.6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}). The proposed regional approach outperforms global personalization when 12-lead ECG is the only electrophysiology data available. Furthermore, promising results of a preliminary CRT study on one patient demonstrate the predictive power of the personalized model.
9 schema:editor Nf1dfc3730eaf4fc0bd4b6e799f92764b
10 schema:genre chapter
11 schema:inLanguage en
12 schema:isAccessibleForFree false
13 schema:isPartOf N6a675c78d46d4d0a9c5336ad5ce88113
14 schema:keywords CRT studies
15 ECG
16 ECG data
17 ECG features
18 QRS
19 Regional electrical properties
20 activation maps
21 activation time
22 applicability
23 approach
24 axis
25 body surface potential mapping
26 cardiac depolarization matches
27 cardiac electrophysiology
28 cardiac images
29 cardiac resynchronization therapy
30 cardiac strain map
31 cardiomyopathy patients
32 clinical applicability
33 clinical images
34 computational model
35 current methods
36 data
37 demonstrate
38 depolarization matches
39 diagnostic ECG
40 diffusivity
41 duration
42 dynamic cardiac images
43 electrical axis
44 electrical diffusivity
45 electrical properties
46 electrocardiography data
47 electrophysiology
48 electrophysiology data
49 electrophysiology study
50 error
51 estimation
52 features
53 fit
54 global personalization
55 heart
56 images
57 individual patients
58 information
59 invasive electrophysiology study
60 lowest mean error
61 mapping
62 maps
63 match
64 mean error
65 mechanical activation map
66 mechanical activation time
67 method
68 model
69 model personalization
70 motion information
71 only electrophysiology data
72 paper
73 parameters
74 patient demonstrate
75 patient selection
76 patients
77 personalization
78 personalized model
79 physiology
80 planning
81 planning of therapy
82 potential mapping
83 power
84 predictive power
85 preliminary CRT study
86 promising results
87 properties
88 regional approach
89 regional electrical diffusivities
90 results
91 resynchronization therapy
92 routine ECG data
93 selection
94 strain maps
95 study
96 surface potential mapping
97 terms
98 therapy
99 time
100 schema:name Estimation of Regional Electrical Properties of the Heart from 12-Lead ECG and Images
101 schema:pagination 204-212
102 schema:productId N58bcb969364048249c5608133b975d31
103 Nf82aa5dcbf544b2c9d2acd9a280162d8
104 schema:publisher N54a8381c1f564745baca6e0abbf2a873
105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050554111
106 https://doi.org/10.1007/978-3-319-14678-2_21
107 schema:sdDatePublished 2022-01-01T19:24
108 schema:sdLicense https://scigraph.springernature.com/explorer/license/
109 schema:sdPublisher N5d2cb588355e43389b4a9a3245992330
110 schema:url https://doi.org/10.1007/978-3-319-14678-2_21
111 sgo:license sg:explorer/license/
112 sgo:sdDataset chapters
113 rdf:type schema:Chapter
114 N15df3de31920477382aec7baaa5036e7 rdf:first sg:person.01066111014.77
115 rdf:rest rdf:nil
116 N16e2b8b084df4b298af1b5bb11e9df29 rdf:first sg:person.0644724502.05
117 rdf:rest Nd04addb942444e2b93f6c1469820f77c
118 N23daa15fc407411cbdd59391dee7bbc3 rdf:first Ndc7e81299b794ec9820b887121dba91a
119 rdf:rest Nd36c1cd655ea41f6837eaed61db62b20
120 N33d13085e3cc473ba0a7372e6b12cad4 rdf:first Nb207187e22a840fa9a5b74731c08741a
121 rdf:rest rdf:nil
122 N3462c3decd574bbeae94e00f4d200911 schema:familyName Rhode
123 schema:givenName Kawal
124 rdf:type schema:Person
125 N3af318762b1c4107aefe7aff824b6bf0 schema:familyName Sermesant
126 schema:givenName Maxime
127 rdf:type schema:Person
128 N4d4520cfe71946edbde8220db59b2e16 schema:familyName Pop
129 schema:givenName Mihaela
130 rdf:type schema:Person
131 N54a8381c1f564745baca6e0abbf2a873 schema:name Springer Nature
132 rdf:type schema:Organisation
133 N58bcb969364048249c5608133b975d31 schema:name doi
134 schema:value 10.1007/978-3-319-14678-2_21
135 rdf:type schema:PropertyValue
136 N5a22d95ba8874fd8892f9ca265ab73d9 schema:familyName Camara
137 schema:givenName Oscar
138 rdf:type schema:Person
139 N5d2cb588355e43389b4a9a3245992330 schema:name Springer Nature - SN SciGraph project
140 rdf:type schema:Organization
141 N6a675c78d46d4d0a9c5336ad5ce88113 schema:isbn 978-3-319-14677-5
142 978-3-319-14678-2
143 schema:name Statistical Atlases and Computational Models of the Heart - Imaging and Modelling Challenges
144 rdf:type schema:Book
145 N7fa2476767fd422885f74a801bd5fdc2 rdf:first sg:person.016076162221.00
146 rdf:rest Nc0265e2c77b9421f9ecb8c2d0a1fbaf1
147 N821251ea43f646739a5076e50ed40e78 rdf:first N3af318762b1c4107aefe7aff824b6bf0
148 rdf:rest N33d13085e3cc473ba0a7372e6b12cad4
149 N8ac60a0dc15c461bbb39c756bc160b5c rdf:first N3462c3decd574bbeae94e00f4d200911
150 rdf:rest N821251ea43f646739a5076e50ed40e78
151 Na93fb4002d044624a0e19fad8e0ad90f rdf:first sg:person.01201613000.02
152 rdf:rest N16e2b8b084df4b298af1b5bb11e9df29
153 Nae1da74d2a2b4b56aa27b0f106ef2c3e rdf:first sg:person.0614041027.22
154 rdf:rest Ne5acfadda60a42e6af6830e97a37f691
155 Nae21958c542749d8ade396f835571596 rdf:first sg:person.0656777564.42
156 rdf:rest Na93fb4002d044624a0e19fad8e0ad90f
157 Nb207187e22a840fa9a5b74731c08741a schema:familyName Young
158 schema:givenName Alistair
159 rdf:type schema:Person
160 Nb61eff9fbe1942a5b8d17bf7e831504d rdf:first sg:person.01173725567.20
161 rdf:rest Nfe0761aaa0d74a66a9da897959fb22dd
162 Nb70f7dafdaa94ee8bd16da8f3977aca6 rdf:first sg:person.0703547214.37
163 rdf:rest Nae21958c542749d8ade396f835571596
164 Nc0265e2c77b9421f9ecb8c2d0a1fbaf1 rdf:first sg:person.01217474726.73
165 rdf:rest Nae1da74d2a2b4b56aa27b0f106ef2c3e
166 Nd04addb942444e2b93f6c1469820f77c rdf:first sg:person.01247726200.41
167 rdf:rest Nb61eff9fbe1942a5b8d17bf7e831504d
168 Nd36c1cd655ea41f6837eaed61db62b20 rdf:first N4d4520cfe71946edbde8220db59b2e16
169 rdf:rest N8ac60a0dc15c461bbb39c756bc160b5c
170 Ndc7e81299b794ec9820b887121dba91a schema:familyName Mansi
171 schema:givenName Tommaso
172 rdf:type schema:Person
173 Ne5acfadda60a42e6af6830e97a37f691 rdf:first sg:person.01054566020.28
174 rdf:rest Nb70f7dafdaa94ee8bd16da8f3977aca6
175 Neda364dfa81d4440b319125ac345d00f rdf:first sg:person.01027273360.08
176 rdf:rest N15df3de31920477382aec7baaa5036e7
177 Nf1dfc3730eaf4fc0bd4b6e799f92764b rdf:first N5a22d95ba8874fd8892f9ca265ab73d9
178 rdf:rest N23daa15fc407411cbdd59391dee7bbc3
179 Nf82aa5dcbf544b2c9d2acd9a280162d8 schema:name dimensions_id
180 schema:value pub.1050554111
181 rdf:type schema:PropertyValue
182 Nfe0761aaa0d74a66a9da897959fb22dd rdf:first sg:person.011260235657.38
183 rdf:rest Neda364dfa81d4440b319125ac345d00f
184 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
185 schema:name Information and Computing Sciences
186 rdf:type schema:DefinedTerm
187 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
188 schema:name Artificial Intelligence and Image Processing
189 rdf:type schema:DefinedTerm
190 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
191 schema:name Medical and Health Sciences
192 rdf:type schema:DefinedTerm
193 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
194 schema:name Cardiorespiratory Medicine and Haematology
195 rdf:type schema:DefinedTerm
196 sg:person.01027273360.08 schema:affiliation grid-institutes:grid.5253.1
197 schema:familyName Meder
198 schema:givenName Benjamin
199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027273360.08
200 rdf:type schema:Person
201 sg:person.01054566020.28 schema:affiliation grid-institutes:grid.5330.5
202 schema:familyName Neumann
203 schema:givenName Dominik
204 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01054566020.28
205 rdf:type schema:Person
206 sg:person.01066111014.77 schema:affiliation grid-institutes:None
207 schema:familyName Comaniciu
208 schema:givenName Dorin
209 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066111014.77
210 rdf:type schema:Person
211 sg:person.011260235657.38 schema:affiliation grid-institutes:grid.5253.1
212 schema:familyName Katus
213 schema:givenName Hugo
214 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011260235657.38
215 rdf:type schema:Person
216 sg:person.01173725567.20 schema:affiliation grid-institutes:grid.5253.1
217 schema:familyName Haas
218 schema:givenName Jan
219 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01173725567.20
220 rdf:type schema:Person
221 sg:person.01201613000.02 schema:affiliation grid-institutes:grid.5253.1
222 schema:familyName Kayvanpour
223 schema:givenName Elham
224 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01201613000.02
225 rdf:type schema:Person
226 sg:person.01217474726.73 schema:affiliation grid-institutes:None
227 schema:familyName Mansi
228 schema:givenName Tommaso
229 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01217474726.73
230 rdf:type schema:Person
231 sg:person.01247726200.41 schema:affiliation grid-institutes:grid.5253.1
232 schema:familyName Sedaghat-Hamedani
233 schema:givenName Farbod
234 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01247726200.41
235 rdf:type schema:Person
236 sg:person.016076162221.00 schema:affiliation grid-institutes:grid.5330.5
237 schema:familyName Seegerer
238 schema:givenName Philipp
239 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016076162221.00
240 rdf:type schema:Person
241 sg:person.0614041027.22 schema:affiliation grid-institutes:None
242 schema:familyName Jolly
243 schema:givenName Marie-Pierre
244 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0614041027.22
245 rdf:type schema:Person
246 sg:person.0644724502.05 schema:affiliation grid-institutes:grid.5253.1
247 schema:familyName Amr
248 schema:givenName Ali
249 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0644724502.05
250 rdf:type schema:Person
251 sg:person.0656777564.42 schema:affiliation grid-institutes:None
252 schema:familyName Kamen
253 schema:givenName Ali
254 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0656777564.42
255 rdf:type schema:Person
256 sg:person.0703547214.37 schema:affiliation grid-institutes:None
257 schema:familyName Georgescu
258 schema:givenName Bogdan
259 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0703547214.37
260 rdf:type schema:Person
261 grid-institutes:None schema:alternateName Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA
262 schema:name Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA
263 rdf:type schema:Organization
264 grid-institutes:grid.5253.1 schema:alternateName Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany
265 schema:name Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany
266 rdf:type schema:Organization
267 grid-institutes:grid.5330.5 schema:alternateName Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany
268 schema:name Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, USA
269 Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany
270 rdf:type schema:Organization
 




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


...