A statistical atlas of cerebral arteries generated using multi-center MRA datasets from healthy subjects. View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-12

AUTHORS

Pauline Mouches, Nils D Forkert

ABSTRACT

Magnetic resonance angiography (MRA) can capture the variation of cerebral arteries with high spatial resolution. These measurements include valuable information about the morphology, geometry, and density of brain arteries, which may be useful to identify risk factors for cerebrovascular and neurological diseases at an early time point. However, this requires knowledge about the distribution and morphology of vessels in healthy subjects. The statistical arterial brain atlas described in this work is a free and public neuroimaging resource that can be used to identify vascular morphological changes. The atlas was generated based on 544 freely available multi-center MRA and T1-weighted MRI datasets. The arteries were automatically segmented in each MRA dataset and used for vessel radius quantification. The binary segmentation and vessel size information were non-linearly registered to the MNI brain atlas using the T1-weighted MRI datasets to construct atlases of artery occurrence probability, mean artery radius, and artery radius standard deviation. This public neuroimaging resource improves the understanding of the distribution and size of arteries in the healthy human brain. More... »

PAGES

29

Journal

TITLE

Scientific Data

ISSUE

1

VOLUME

6

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41597-019-0034-5

DOI

http://dx.doi.org/10.1038/s41597-019-0034-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113378282

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30975990


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/2008/1109", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Neurosciences", 
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Calgary", 
          "id": "https://www.grid.ac/institutes/grid.22072.35", 
          "name": [
            "Department of Radiology, University of Calgary, Calgary, Canada. pauline.mouches@ucalgary.ca.", 
            "Hotchkiss Brain Institute, University of Calgary, Calgary, Canada. pauline.mouches@ucalgary.ca."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mouches", 
        "givenName": "Pauline", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Calgary", 
          "id": "https://www.grid.ac/institutes/grid.22072.35", 
          "name": [
            "Department of Radiology, University of Calgary, Calgary, Canada. nils.forkert@ucalgary.ca.", 
            "Hotchkiss Brain Institute, University of Calgary, Calgary, Canada. nils.forkert@ucalgary.ca."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Forkert", 
        "givenName": "Nils D", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s12021-016-9320-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002411168", 
          "https://doi.org/10.1007/s12021-016-9320-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12021-016-9320-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002411168", 
          "https://doi.org/10.1007/s12021-016-9320-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cmpb.2009.09.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008920304"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.mri.2012.07.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014990864"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0146-664x(80)90054-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017627930"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rstb.2001.0915", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020547766"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.nic.2012.02.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039275422"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1474-4422(13)70124-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049427712"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1474-4422(13)70124-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049427712"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1361-8415(98)80009-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051898748"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/42.836373", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061170896"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1976.45.3.0259", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071090982"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1976.45.3.0259", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071090982"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1976.45.3.0259", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071090982"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1977.46.5.0563", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071091156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1977.46.5.0563", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071091156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1977.46.5.0563", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071091156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1978.48.4.0534", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071091434"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1978.48.4.0534", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071091434"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1978.48.4.0534", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071091434"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1981.54.2.0151", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071092341"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1981.54.2.0151", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071092341"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.1981.54.2.0151", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071092341"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.2000.92.4.0676", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071099520"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.2000.92.4.0676", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071099520"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3171/jns.2000.92.4.0676", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071099520"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3414/me10-02-0003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071312091"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3414/me13-02-0001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071312243"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1076945049", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1079546064", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "Magnetic resonance angiography (MRA) can capture the variation of cerebral arteries with high spatial resolution. These measurements include valuable information about the morphology, geometry, and density of brain arteries, which may be useful to identify risk factors for cerebrovascular and neurological diseases at an early time point. However, this requires knowledge about the distribution and morphology of vessels in healthy subjects. The statistical arterial brain atlas described in this work is a free and public neuroimaging resource that can be used to identify vascular morphological changes. The atlas was generated based on 544 freely available multi-center MRA and T1-weighted MRI datasets. The arteries were automatically segmented in each MRA dataset and used for vessel radius quantification. The binary segmentation and vessel size information were non-linearly registered to the MNI brain atlas using the T1-weighted MRI datasets to construct atlases of artery occurrence probability, mean artery radius, and artery radius standard deviation. This public neuroimaging resource improves the understanding of the distribution and size of arteries in the healthy human brain.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41597-019-0034-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1050678", 
        "issn": [
          "2052-4463"
        ], 
        "name": "Scientific Data", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "6"
      }
    ], 
    "name": "A statistical atlas of cerebral arteries generated using multi-center MRA datasets from healthy subjects.", 
    "pagination": "29", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41597-019-0034-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1113378282"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101640192"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30975990"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41597-019-0034-5", 
      "https://app.dimensions.ai/details/publication/pub.1113378282"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-16T06:25", 
    "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/v90/0000000377_0000000377/records_106837_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://www.nature.com/articles/s41597-019-0034-5"
  }
]
 

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.10.1038/s41597-019-0034-5'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41597-019-0034-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41597-019-0034-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41597-019-0034-5'


 

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

125 TRIPLES      21 PREDICATES      46 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41597-019-0034-5 schema:about anzsrc-for:11
2 anzsrc-for:1109
3 schema:author Na651bb82e23e4933b332476ae824e771
4 schema:citation sg:pub.10.1007/s12021-016-9320-y
5 https://app.dimensions.ai/details/publication/pub.1076945049
6 https://app.dimensions.ai/details/publication/pub.1079546064
7 https://doi.org/10.1016/0146-664x(80)90054-4
8 https://doi.org/10.1016/j.cmpb.2009.09.002
9 https://doi.org/10.1016/j.mri.2012.07.008
10 https://doi.org/10.1016/j.nic.2012.02.006
11 https://doi.org/10.1016/s1361-8415(98)80009-1
12 https://doi.org/10.1016/s1474-4422(13)70124-8
13 https://doi.org/10.1098/rstb.2001.0915
14 https://doi.org/10.1109/42.836373
15 https://doi.org/10.3171/jns.1976.45.3.0259
16 https://doi.org/10.3171/jns.1977.46.5.0563
17 https://doi.org/10.3171/jns.1978.48.4.0534
18 https://doi.org/10.3171/jns.1981.54.2.0151
19 https://doi.org/10.3171/jns.2000.92.4.0676
20 https://doi.org/10.3414/me10-02-0003
21 https://doi.org/10.3414/me13-02-0001
22 schema:datePublished 2019-12
23 schema:datePublishedReg 2019-12-01
24 schema:description Magnetic resonance angiography (MRA) can capture the variation of cerebral arteries with high spatial resolution. These measurements include valuable information about the morphology, geometry, and density of brain arteries, which may be useful to identify risk factors for cerebrovascular and neurological diseases at an early time point. However, this requires knowledge about the distribution and morphology of vessels in healthy subjects. The statistical arterial brain atlas described in this work is a free and public neuroimaging resource that can be used to identify vascular morphological changes. The atlas was generated based on 544 freely available multi-center MRA and T1-weighted MRI datasets. The arteries were automatically segmented in each MRA dataset and used for vessel radius quantification. The binary segmentation and vessel size information were non-linearly registered to the MNI brain atlas using the T1-weighted MRI datasets to construct atlases of artery occurrence probability, mean artery radius, and artery radius standard deviation. This public neuroimaging resource improves the understanding of the distribution and size of arteries in the healthy human brain.
25 schema:genre research_article
26 schema:inLanguage en
27 schema:isAccessibleForFree false
28 schema:isPartOf N6a3ba6abf1d842d3987acc6656eb33e5
29 Ndf3d30bddc6c4e6092d5209ea8e66798
30 sg:journal.1050678
31 schema:name A statistical atlas of cerebral arteries generated using multi-center MRA datasets from healthy subjects.
32 schema:pagination 29
33 schema:productId N5164b25085b349a4acd9b010444a757a
34 N7ff6aa7c332b4d699c0fdccb799113cd
35 N9170a17e90034a71b9f97b6085fb9130
36 Nd335867739694fd59dfec35615afb215
37 schema:sameAs https://app.dimensions.ai/details/publication/pub.1113378282
38 https://doi.org/10.1038/s41597-019-0034-5
39 schema:sdDatePublished 2019-04-16T06:25
40 schema:sdLicense https://scigraph.springernature.com/explorer/license/
41 schema:sdPublisher N5d1d1eeaaf184733985d4c57b58898ee
42 schema:url http://www.nature.com/articles/s41597-019-0034-5
43 sgo:license sg:explorer/license/
44 sgo:sdDataset articles
45 rdf:type schema:ScholarlyArticle
46 N1e13eebabb6246698877b0dd5955c187 schema:affiliation https://www.grid.ac/institutes/grid.22072.35
47 schema:familyName Mouches
48 schema:givenName Pauline
49 rdf:type schema:Person
50 N5164b25085b349a4acd9b010444a757a schema:name dimensions_id
51 schema:value pub.1113378282
52 rdf:type schema:PropertyValue
53 N5d1d1eeaaf184733985d4c57b58898ee schema:name Springer Nature - SN SciGraph project
54 rdf:type schema:Organization
55 N6a3ba6abf1d842d3987acc6656eb33e5 schema:issueNumber 1
56 rdf:type schema:PublicationIssue
57 N7ff6aa7c332b4d699c0fdccb799113cd schema:name pubmed_id
58 schema:value 30975990
59 rdf:type schema:PropertyValue
60 N9170a17e90034a71b9f97b6085fb9130 schema:name nlm_unique_id
61 schema:value 101640192
62 rdf:type schema:PropertyValue
63 Na651bb82e23e4933b332476ae824e771 rdf:first N1e13eebabb6246698877b0dd5955c187
64 rdf:rest Nd81e2bc11eb54bd2a5858f147284f73c
65 Nd335867739694fd59dfec35615afb215 schema:name doi
66 schema:value 10.1038/s41597-019-0034-5
67 rdf:type schema:PropertyValue
68 Nd81e2bc11eb54bd2a5858f147284f73c rdf:first Ne7276ed1dcd44471b7ab803563f56a2a
69 rdf:rest rdf:nil
70 Ndf3d30bddc6c4e6092d5209ea8e66798 schema:volumeNumber 6
71 rdf:type schema:PublicationVolume
72 Ne7276ed1dcd44471b7ab803563f56a2a schema:affiliation https://www.grid.ac/institutes/grid.22072.35
73 schema:familyName Forkert
74 schema:givenName Nils D
75 rdf:type schema:Person
76 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
77 schema:name Medical and Health Sciences
78 rdf:type schema:DefinedTerm
79 anzsrc-for:1109 schema:inDefinedTermSet anzsrc-for:
80 schema:name Neurosciences
81 rdf:type schema:DefinedTerm
82 sg:journal.1050678 schema:issn 2052-4463
83 schema:name Scientific Data
84 rdf:type schema:Periodical
85 sg:pub.10.1007/s12021-016-9320-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1002411168
86 https://doi.org/10.1007/s12021-016-9320-y
87 rdf:type schema:CreativeWork
88 https://app.dimensions.ai/details/publication/pub.1076945049 schema:CreativeWork
89 https://app.dimensions.ai/details/publication/pub.1079546064 schema:CreativeWork
90 https://doi.org/10.1016/0146-664x(80)90054-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017627930
91 rdf:type schema:CreativeWork
92 https://doi.org/10.1016/j.cmpb.2009.09.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008920304
93 rdf:type schema:CreativeWork
94 https://doi.org/10.1016/j.mri.2012.07.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014990864
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1016/j.nic.2012.02.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039275422
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1016/s1361-8415(98)80009-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051898748
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1016/s1474-4422(13)70124-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049427712
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1098/rstb.2001.0915 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020547766
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1109/42.836373 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061170896
105 rdf:type schema:CreativeWork
106 https://doi.org/10.3171/jns.1976.45.3.0259 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071090982
107 rdf:type schema:CreativeWork
108 https://doi.org/10.3171/jns.1977.46.5.0563 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071091156
109 rdf:type schema:CreativeWork
110 https://doi.org/10.3171/jns.1978.48.4.0534 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071091434
111 rdf:type schema:CreativeWork
112 https://doi.org/10.3171/jns.1981.54.2.0151 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071092341
113 rdf:type schema:CreativeWork
114 https://doi.org/10.3171/jns.2000.92.4.0676 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071099520
115 rdf:type schema:CreativeWork
116 https://doi.org/10.3414/me10-02-0003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071312091
117 rdf:type schema:CreativeWork
118 https://doi.org/10.3414/me13-02-0001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071312243
119 rdf:type schema:CreativeWork
120 https://www.grid.ac/institutes/grid.22072.35 schema:alternateName University of Calgary
121 schema:name Department of Radiology, University of Calgary, Calgary, Canada. nils.forkert@ucalgary.ca.
122 Department of Radiology, University of Calgary, Calgary, Canada. pauline.mouches@ucalgary.ca.
123 Hotchkiss Brain Institute, University of Calgary, Calgary, Canada. nils.forkert@ucalgary.ca.
124 Hotchkiss Brain Institute, University of Calgary, Calgary, Canada. pauline.mouches@ucalgary.ca.
125 rdf:type schema:Organization
 




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


...