Accelerated multi-contrast high isotropic resolution 3D intracranial vessel wall MRI using a tailored k-space undersampling and partially parallel reconstruction strategy View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-01-03

AUTHORS

Niranjan Balu, Zechen Zhou, Daniel S. Hippe, Thomas Hatsukami, Mahmud Mossa-Basha, Chun Yuan

ABSTRACT

OBJECTIVE: To develop a 3D multi-contrast IVW protocol with 0.5-mm isotropic resolution and a scan time of 5 min per sequence. MATERIALS AND METHODS: Pre-contrast T1w VISTA, DANTE prepared PDw VISTA, SNAP, and post-contrast T1w VISTA were accelerated using cartesian undersampling with target ordering method (CUSTOM) and self-supporting tailored k-space estimation for parallel imaging reconstruction (STEP). CUSTOM + STEP IVW was compared to full-sample IVW, SENSE-accelerated IVW, and CUSTOM + zero-filled Fourier reconstruction in normal volunteers and subjects with intracranial atherosclerotic disease (ICAD). Image quality, vessel delineation, CSF suppression, and blood suppression were compared. RESULTS: CUSTOM + STEP vessel wall delineation was comparable to full-sample IVW and better than SENSE IVW for vessel wall delineation on T1w VISTA and luminal contrast on SNAP. Average image quality and wall depiction were significantly improved using STEP reconstruction compared with zero-filled Fourier reconstruction, with no significant difference in CSF or blood suppression. CONCLUSIONS: CUSTOM + STEP allowed multi-contrast 3D 0.5-mm isotropic IVW within 30 min. Although some quantitative and qualitative scores for CUSTOM - STEP were lower than fully sampled IVW, CUSTOM + STEP provided comparable vessel wall delineation as full-sample IVW and was superior to SENSE. CUSTOM + STEP IVW was well tolerated by patients and showed good delineation of ICAD plaque. More... »

PAGES

1-15

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10334-018-0730-8

DOI

http://dx.doi.org/10.1007/s10334-018-0730-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111097733

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30607664


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/2008/1102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Cardiorespiratory Medicine and Haematology", 
        "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": {
          "name": [
            "Vascular Imaging Lab, Department of Radiology, University of Washington, 850, Republican Street, Box 358050, 98019, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Balu", 
        "givenName": "Niranjan", 
        "id": "sg:person.0665143653.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0665143653.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Healthcare Department, Philips Research China, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhou", 
        "givenName": "Zechen", 
        "id": "sg:person.0610451615.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610451615.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Vascular Imaging Lab, Department of Radiology, University of Washington, 850, Republican Street, Box 358050, 98019, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hippe", 
        "givenName": "Daniel S.", 
        "id": "sg:person.0764104440.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0764104440.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Washington", 
          "id": "https://www.grid.ac/institutes/grid.34477.33", 
          "name": [
            "Department of Surgery, University of Washington, Seattle, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hatsukami", 
        "givenName": "Thomas", 
        "id": "sg:person.013351427457.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013351427457.97"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Vascular Imaging Lab, Department of Radiology, University of Washington, 850, Republican Street, Box 358050, 98019, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mossa-Basha", 
        "givenName": "Mahmud", 
        "id": "sg:person.01174002613.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01174002613.22"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Vascular Imaging Lab, Department of Radiology, University of Washington, 850, Republican Street, Box 358050, 98019, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yuan", 
        "givenName": "Chun", 
        "id": "sg:person.011712252247.13", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011712252247.13"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/mrm.25785", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002669954"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.24254", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003250586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.13122812", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005248032"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rli.0b013e3181beada7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005548595"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rli.0b013e3181beada7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005548595"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rli.0b013e3181beada7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005548595"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/jnnp-2015-312020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008209280"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.111.618132", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018155790"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.111.618132", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018155790"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12968-015-0143-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018210427", 
          "https://doi.org/10.1186/s12968-015-0143-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12968-015-0143-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018210427", 
          "https://doi.org/10.1186/s12968-015-0143-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1159/000209238", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019860850"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.22592", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024044735"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.25667", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025561083"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0160781", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025956314"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.24997", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027593175"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.115.009037", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028245380"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.115.009037", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028245380"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.09090535", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032991700"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.mri.2015.06.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035671244"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.21391", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037838340"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.22428", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040909232"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.22428", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040909232"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.24142", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042401520"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.114.006626", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042918624"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.114.006626", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042918624"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.25663", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047717444"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3174/ajnr.a4893", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049112262"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3174/ajnr.a4893", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049112262"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2015/356582", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050340175"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-013-2905-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050577405", 
          "https://doi.org/10.1007/s00330-013-2905-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.116.013007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050886583"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.116.013007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050886583"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.111.620443", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052887979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.111.620443", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052887979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/cns.12224", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053613221"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/lsp.2013.2248711", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061378355"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.116.013320", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063345911"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/strokeaha.116.013320", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063345911"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1212/01.wnl.0000225074.47396.71", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064348292"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1212/01.wnl.0000225074.47396.71", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064348292"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1212/01.wnl.0000225074.47396.71", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064348292"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-01-03", 
    "datePublishedReg": "2019-01-03", 
    "description": "OBJECTIVE: To develop a 3D multi-contrast IVW protocol with 0.5-mm isotropic resolution and a scan time of 5\u00a0min per sequence.\nMATERIALS AND METHODS: Pre-contrast T1w VISTA, DANTE prepared PDw VISTA, SNAP, and post-contrast T1w VISTA were accelerated using cartesian undersampling with target ordering method (CUSTOM) and self-supporting tailored k-space estimation for parallel imaging reconstruction (STEP). CUSTOM\u2009+\u2009STEP IVW was compared to full-sample IVW, SENSE-accelerated IVW, and CUSTOM\u2009+\u2009zero-filled Fourier reconstruction in normal volunteers and subjects with intracranial atherosclerotic disease (ICAD). Image quality, vessel delineation, CSF suppression, and blood suppression were compared.\nRESULTS: CUSTOM\u2009+\u2009STEP vessel wall delineation was comparable to full-sample IVW and better than SENSE IVW for vessel wall delineation on T1w VISTA and luminal contrast on SNAP. Average image quality and wall depiction were significantly improved using STEP reconstruction compared with zero-filled Fourier reconstruction, with no significant difference in CSF or blood suppression.\nCONCLUSIONS: CUSTOM\u2009+\u2009STEP allowed multi-contrast 3D 0.5-mm isotropic IVW within 30\u00a0min. Although some quantitative and qualitative scores for CUSTOM\u00a0-\u00a0STEP were lower than fully sampled IVW, CUSTOM\u2009+\u2009STEP provided comparable vessel wall delineation as full-sample IVW and was superior to SENSE. CUSTOM\u2009+\u2009STEP IVW was well tolerated by patients and showed good delineation of ICAD plaque.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10334-018-0730-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.4102062", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5476644", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1104145", 
        "issn": [
          "0968-5243", 
          "1352-8661"
        ], 
        "name": "Magnetic Resonance Materials in Physics, Biology and Medicine", 
        "type": "Periodical"
      }
    ], 
    "name": "Accelerated multi-contrast high isotropic resolution 3D intracranial vessel wall MRI using a tailored k-space undersampling and partially parallel reconstruction strategy", 
    "pagination": "1-15", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "2bc97772621bd34c372bc72b1acd047bbd32c824ab83964ea71de9ff76f83636"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30607664"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9310752"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10334-018-0730-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1111097733"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10334-018-0730-8", 
      "https://app.dimensions.ai/details/publication/pub.1111097733"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T08:34", 
    "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/v90/0000000311_0000000311/records_55454_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10334-018-0730-8"
  }
]
 

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/s10334-018-0730-8'

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/s10334-018-0730-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10334-018-0730-8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10334-018-0730-8'


 

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

201 TRIPLES      21 PREDICATES      55 URIs      18 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10334-018-0730-8 schema:about anzsrc-for:11
2 anzsrc-for:1102
3 schema:author N0078637bb87a44828b632987c79104b0
4 schema:citation sg:pub.10.1007/s00330-013-2905-z
5 sg:pub.10.1186/s12968-015-0143-z
6 https://doi.org/10.1002/jmri.22592
7 https://doi.org/10.1002/mrm.21391
8 https://doi.org/10.1002/mrm.22428
9 https://doi.org/10.1002/mrm.24142
10 https://doi.org/10.1002/mrm.24254
11 https://doi.org/10.1002/mrm.24997
12 https://doi.org/10.1002/mrm.25663
13 https://doi.org/10.1002/mrm.25667
14 https://doi.org/10.1002/mrm.25785
15 https://doi.org/10.1016/j.mri.2015.06.006
16 https://doi.org/10.1097/rli.0b013e3181beada7
17 https://doi.org/10.1109/lsp.2013.2248711
18 https://doi.org/10.1111/cns.12224
19 https://doi.org/10.1136/jnnp-2015-312020
20 https://doi.org/10.1148/radiol.09090535
21 https://doi.org/10.1148/radiol.13122812
22 https://doi.org/10.1155/2015/356582
23 https://doi.org/10.1159/000209238
24 https://doi.org/10.1161/strokeaha.111.618132
25 https://doi.org/10.1161/strokeaha.111.620443
26 https://doi.org/10.1161/strokeaha.114.006626
27 https://doi.org/10.1161/strokeaha.115.009037
28 https://doi.org/10.1161/strokeaha.116.013007
29 https://doi.org/10.1161/strokeaha.116.013320
30 https://doi.org/10.1212/01.wnl.0000225074.47396.71
31 https://doi.org/10.1371/journal.pone.0160781
32 https://doi.org/10.3174/ajnr.a4893
33 schema:datePublished 2019-01-03
34 schema:datePublishedReg 2019-01-03
35 schema:description OBJECTIVE: To develop a 3D multi-contrast IVW protocol with 0.5-mm isotropic resolution and a scan time of 5 min per sequence. MATERIALS AND METHODS: Pre-contrast T1w VISTA, DANTE prepared PDw VISTA, SNAP, and post-contrast T1w VISTA were accelerated using cartesian undersampling with target ordering method (CUSTOM) and self-supporting tailored k-space estimation for parallel imaging reconstruction (STEP). CUSTOM + STEP IVW was compared to full-sample IVW, SENSE-accelerated IVW, and CUSTOM + zero-filled Fourier reconstruction in normal volunteers and subjects with intracranial atherosclerotic disease (ICAD). Image quality, vessel delineation, CSF suppression, and blood suppression were compared. RESULTS: CUSTOM + STEP vessel wall delineation was comparable to full-sample IVW and better than SENSE IVW for vessel wall delineation on T1w VISTA and luminal contrast on SNAP. Average image quality and wall depiction were significantly improved using STEP reconstruction compared with zero-filled Fourier reconstruction, with no significant difference in CSF or blood suppression. CONCLUSIONS: CUSTOM + STEP allowed multi-contrast 3D 0.5-mm isotropic IVW within 30 min. Although some quantitative and qualitative scores for CUSTOM - STEP were lower than fully sampled IVW, CUSTOM + STEP provided comparable vessel wall delineation as full-sample IVW and was superior to SENSE. CUSTOM + STEP IVW was well tolerated by patients and showed good delineation of ICAD plaque.
36 schema:genre research_article
37 schema:inLanguage en
38 schema:isAccessibleForFree false
39 schema:isPartOf sg:journal.1104145
40 schema:name Accelerated multi-contrast high isotropic resolution 3D intracranial vessel wall MRI using a tailored k-space undersampling and partially parallel reconstruction strategy
41 schema:pagination 1-15
42 schema:productId N2baf9189e8e04d3f819c1e5fd6a81e3e
43 N5d637333209b4765a99c2cf95f2a39d6
44 N74e6de12073546de8ddf8a3ec7efe808
45 N875a4037495e47cf83e1e2b4b7df7298
46 Nc6b5695539e947e297ae252b5ecff1d1
47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111097733
48 https://doi.org/10.1007/s10334-018-0730-8
49 schema:sdDatePublished 2019-04-11T08:34
50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
51 schema:sdPublisher Nc0016902fad849fa969f2ddd27a5d54f
52 schema:url https://link.springer.com/10.1007%2Fs10334-018-0730-8
53 sgo:license sg:explorer/license/
54 sgo:sdDataset articles
55 rdf:type schema:ScholarlyArticle
56 N0078637bb87a44828b632987c79104b0 rdf:first sg:person.0665143653.55
57 rdf:rest N815759f179884d77a597291e1ca08f1d
58 N1a0f805e2b404035ac62a564d8fa7c01 schema:name Vascular Imaging Lab, Department of Radiology, University of Washington, 850, Republican Street, Box 358050, 98019, Seattle, WA, USA
59 rdf:type schema:Organization
60 N283118fd99d34c409badf2ac7b6a4cc1 rdf:first sg:person.011712252247.13
61 rdf:rest rdf:nil
62 N2baf9189e8e04d3f819c1e5fd6a81e3e schema:name dimensions_id
63 schema:value pub.1111097733
64 rdf:type schema:PropertyValue
65 N4f64c2a41d034204ba4a31f04fdd856e schema:name Vascular Imaging Lab, Department of Radiology, University of Washington, 850, Republican Street, Box 358050, 98019, Seattle, WA, USA
66 rdf:type schema:Organization
67 N58cea23ae0254687ade1a652f19e7059 schema:name Vascular Imaging Lab, Department of Radiology, University of Washington, 850, Republican Street, Box 358050, 98019, Seattle, WA, USA
68 rdf:type schema:Organization
69 N5d637333209b4765a99c2cf95f2a39d6 schema:name readcube_id
70 schema:value 2bc97772621bd34c372bc72b1acd047bbd32c824ab83964ea71de9ff76f83636
71 rdf:type schema:PropertyValue
72 N65d464839bf3458788794a5d8ec89a59 schema:name Vascular Imaging Lab, Department of Radiology, University of Washington, 850, Republican Street, Box 358050, 98019, Seattle, WA, USA
73 rdf:type schema:Organization
74 N74e6de12073546de8ddf8a3ec7efe808 schema:name nlm_unique_id
75 schema:value 9310752
76 rdf:type schema:PropertyValue
77 N815759f179884d77a597291e1ca08f1d rdf:first sg:person.0610451615.17
78 rdf:rest Nf8271011ced144df8d80587bb6d23a5e
79 N875a4037495e47cf83e1e2b4b7df7298 schema:name pubmed_id
80 schema:value 30607664
81 rdf:type schema:PropertyValue
82 Nb63161d687954eaca60293cf7dbe5766 rdf:first sg:person.013351427457.97
83 rdf:rest Nbb686f3200504c53889ef44485b75216
84 Nb9c8db002b214a02bb111abe95d10e35 schema:name Healthcare Department, Philips Research China, Shanghai, China
85 rdf:type schema:Organization
86 Nbb686f3200504c53889ef44485b75216 rdf:first sg:person.01174002613.22
87 rdf:rest N283118fd99d34c409badf2ac7b6a4cc1
88 Nc0016902fad849fa969f2ddd27a5d54f schema:name Springer Nature - SN SciGraph project
89 rdf:type schema:Organization
90 Nc6b5695539e947e297ae252b5ecff1d1 schema:name doi
91 schema:value 10.1007/s10334-018-0730-8
92 rdf:type schema:PropertyValue
93 Nf8271011ced144df8d80587bb6d23a5e rdf:first sg:person.0764104440.34
94 rdf:rest Nb63161d687954eaca60293cf7dbe5766
95 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
96 schema:name Medical and Health Sciences
97 rdf:type schema:DefinedTerm
98 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
99 schema:name Cardiorespiratory Medicine and Haematology
100 rdf:type schema:DefinedTerm
101 sg:grant.4102062 http://pending.schema.org/fundedItem sg:pub.10.1007/s10334-018-0730-8
102 rdf:type schema:MonetaryGrant
103 sg:grant.5476644 http://pending.schema.org/fundedItem sg:pub.10.1007/s10334-018-0730-8
104 rdf:type schema:MonetaryGrant
105 sg:journal.1104145 schema:issn 0968-5243
106 1352-8661
107 schema:name Magnetic Resonance Materials in Physics, Biology and Medicine
108 rdf:type schema:Periodical
109 sg:person.011712252247.13 schema:affiliation N58cea23ae0254687ade1a652f19e7059
110 schema:familyName Yuan
111 schema:givenName Chun
112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011712252247.13
113 rdf:type schema:Person
114 sg:person.01174002613.22 schema:affiliation N1a0f805e2b404035ac62a564d8fa7c01
115 schema:familyName Mossa-Basha
116 schema:givenName Mahmud
117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01174002613.22
118 rdf:type schema:Person
119 sg:person.013351427457.97 schema:affiliation https://www.grid.ac/institutes/grid.34477.33
120 schema:familyName Hatsukami
121 schema:givenName Thomas
122 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013351427457.97
123 rdf:type schema:Person
124 sg:person.0610451615.17 schema:affiliation Nb9c8db002b214a02bb111abe95d10e35
125 schema:familyName Zhou
126 schema:givenName Zechen
127 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610451615.17
128 rdf:type schema:Person
129 sg:person.0665143653.55 schema:affiliation N4f64c2a41d034204ba4a31f04fdd856e
130 schema:familyName Balu
131 schema:givenName Niranjan
132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0665143653.55
133 rdf:type schema:Person
134 sg:person.0764104440.34 schema:affiliation N65d464839bf3458788794a5d8ec89a59
135 schema:familyName Hippe
136 schema:givenName Daniel S.
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0764104440.34
138 rdf:type schema:Person
139 sg:pub.10.1007/s00330-013-2905-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1050577405
140 https://doi.org/10.1007/s00330-013-2905-z
141 rdf:type schema:CreativeWork
142 sg:pub.10.1186/s12968-015-0143-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1018210427
143 https://doi.org/10.1186/s12968-015-0143-z
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1002/jmri.22592 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024044735
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1002/mrm.21391 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037838340
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1002/mrm.22428 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040909232
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1002/mrm.24142 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042401520
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1002/mrm.24254 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003250586
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1002/mrm.24997 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027593175
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1002/mrm.25663 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047717444
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1002/mrm.25667 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025561083
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1002/mrm.25785 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002669954
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.mri.2015.06.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035671244
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1097/rli.0b013e3181beada7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005548595
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1109/lsp.2013.2248711 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061378355
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1111/cns.12224 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053613221
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1136/jnnp-2015-312020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008209280
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1148/radiol.09090535 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032991700
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1148/radiol.13122812 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005248032
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1155/2015/356582 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050340175
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1159/000209238 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019860850
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1161/strokeaha.111.618132 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018155790
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1161/strokeaha.111.620443 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052887979
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1161/strokeaha.114.006626 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042918624
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1161/strokeaha.115.009037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028245380
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1161/strokeaha.116.013007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050886583
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1161/strokeaha.116.013320 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063345911
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1212/01.wnl.0000225074.47396.71 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064348292
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1371/journal.pone.0160781 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025956314
196 rdf:type schema:CreativeWork
197 https://doi.org/10.3174/ajnr.a4893 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049112262
198 rdf:type schema:CreativeWork
199 https://www.grid.ac/institutes/grid.34477.33 schema:alternateName University of Washington
200 schema:name Department of Surgery, University of Washington, Seattle, USA
201 rdf:type schema:Organization
 




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


...