Clinical Potential of a New Approach to MRI Acceleration View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Nadine L. Dispenza, Sebastian Littin, Maxim Zaitsev, R. Todd Constable, Gigi Galiana

ABSTRACT

Fast ROtary Nonlinear Spatial ACquisition (FRONSAC) was recently introduced as a new strategy that applies nonlinear gradients as a small perturbation to improve image quality in highly undersampled MRI. In addition to experimentally showing the previously simulated improvement to image quality, this work introduces the insight that Cartesian-FRONSAC retains many desirable features of Cartesian imaging. Cartesian-FRONSAC preserves the existing linear gradient waveforms of the Cartesian sequence while adding oscillating nonlinear gradient waveforms. Experiments show that performance is essentially identical to Cartesian imaging in terms of (1) resilience to experimental imperfections, like timing errors or off-resonance spins, (2) accommodating scan geometry changes without the need for recalibration or additional field mapping, (3) contrast generation, as in turbo spin echo. Despite these similarities to Cartesian imaging, which provides poor parallel imaging performance, Cartesian-FRONSAC consistently shows reduced undersampling artifacts and better response to advanced reconstruction techniques. A final experiment shows that hardware requirements are also flexible. Cartesian-FRONSAC improves accelerated imaging while retaining the robustness and flexibility critical to real clinical use. More... »

PAGES

1912

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-36802-5

DOI

http://dx.doi.org/10.1038/s41598-018-36802-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112094540

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30760731


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/2008/0299", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Other Physical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/02", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Yale University", 
          "id": "https://www.grid.ac/institutes/grid.47100.32", 
          "name": [
            "Department of Biomedical Engineering, Yale University, New Haven, CT, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dispenza", 
        "givenName": "Nadine L.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University Medical Center Freiburg", 
          "id": "https://www.grid.ac/institutes/grid.7708.8", 
          "name": [
            "Department of Diagnostic Radiology, Medical Physics, University Medical Center Freiburg, Breisacher Str. 60a, 79106, Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Littin", 
        "givenName": "Sebastian", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University Medical Center Freiburg", 
          "id": "https://www.grid.ac/institutes/grid.7708.8", 
          "name": [
            "Department of Diagnostic Radiology, Medical Physics, University Medical Center Freiburg, Breisacher Str. 60a, 79106, Freiburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zaitsev", 
        "givenName": "Maxim", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Yale University", 
          "id": "https://www.grid.ac/institutes/grid.47100.32", 
          "name": [
            "Department of Radiology and Biomedical Imaging, Yale University, 06520, New Haven, CT, USA", 
            "Department of Neurosurgery, Yale University, 06520, New Haven, CT, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Constable", 
        "givenName": "R. Todd", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Yale University", 
          "id": "https://www.grid.ac/institutes/grid.47100.32", 
          "name": [
            "Department of Radiology and Biomedical Imaging, Yale University, 06520, New Haven, CT, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Galiana", 
        "givenName": "Gigi", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/mrm.20819", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000131680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.20819", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000131680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.21643", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000647026"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.20320", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002650411"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.20320", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002650411"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.22463", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004356511"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.22463", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004356511"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.26145", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006066834"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.25152", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006183265"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.22672", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007226138"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.25364", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009061597"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.24494", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009345523"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.10171", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012449961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.24282", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014922979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.26235", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015424945"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.1910380414", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018509237"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.25347", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023308255"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ncomms13702", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023322000", 
          "https://doi.org/10.1038/ncomms13702"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/1522-2594(200010)44:4<602::aid-mrm14>3.0.co;2-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025405172"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.24443", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025997937"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.24115", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026454831"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1522-2594(199911)42:5<952::aid-mrm16>3.0.co;2-s", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029323697"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.25423", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030101481"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.24928", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030259736"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/242190a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031397834", 
          "https://doi.org/10.1038/242190a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10334-008-0105-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034760603", 
          "https://doi.org/10.1007/s10334-008-0105-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10334-008-0105-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034760603", 
          "https://doi.org/10.1007/s10334-008-0105-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.23146", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035044168"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.22425", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035737847"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.22425", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035737847"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1098-1098(1999)10:3<216::aid-ima3>3.0.co;2-q", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036803680"
        ], 
        "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.24114", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037910933"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/cmr.a.21243", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038541259"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/cmr.a.21243", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038541259"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.25703", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039826229"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.25235", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041918058"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.25085", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049261401"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/42.611345", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061170548"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/msp.2007.914728", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061422951"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.1986.4307732", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061694123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2017.2650960", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061696872"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsp.2002.807005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061798816"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1259/0007-1285-50-591-188", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064551653"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.26700", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085058132"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.26700", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085058132"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jmr.2017.06.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085962623"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icmipe.2013.6864568", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094261026"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/isbi.2016.7493320", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095595792"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "Fast ROtary Nonlinear Spatial ACquisition (FRONSAC) was recently introduced as a new strategy that applies nonlinear gradients as a small perturbation to improve image quality in highly undersampled MRI. In addition to experimentally showing the previously simulated improvement to image quality, this work introduces the insight that Cartesian-FRONSAC retains many desirable features of Cartesian imaging. Cartesian-FRONSAC preserves the existing linear gradient waveforms of the Cartesian sequence while adding oscillating nonlinear gradient waveforms. Experiments show that performance is essentially identical to Cartesian imaging in terms of (1) resilience to experimental imperfections, like timing errors or off-resonance spins, (2) accommodating scan geometry changes without the need for recalibration or additional field mapping, (3) contrast generation, as in turbo spin echo. Despite these similarities to Cartesian imaging, which provides poor parallel imaging performance, Cartesian-FRONSAC consistently shows reduced undersampling artifacts and better response to advanced reconstruction techniques. A final experiment shows that hardware requirements are also flexible. Cartesian-FRONSAC improves accelerated imaging while retaining the robustness and flexibility critical to real clinical use.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41598-018-36802-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2502018", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5475983", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.7071947", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2502169", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "9"
      }
    ], 
    "name": "Clinical Potential of a New Approach to MRI Acceleration", 
    "pagination": "1912", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "e72af1894651a249c9236a404dc236663a607729caf27faa1f6cc154a1967669"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30760731"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101563288"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41598-018-36802-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112094540"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41598-018-36802-5", 
      "https://app.dimensions.ai/details/publication/pub.1112094540"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:18", 
    "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/v90/0000000368_0000000368/records_78947_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41598-018-36802-5"
  }
]
 

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/s41598-018-36802-5'

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/s41598-018-36802-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-36802-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-36802-5'


 

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

233 TRIPLES      21 PREDICATES      71 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41598-018-36802-5 schema:about anzsrc-for:02
2 anzsrc-for:0299
3 schema:author N0cff706e06e44a709b7717e8782ff92f
4 schema:citation sg:pub.10.1007/s10334-008-0105-7
5 sg:pub.10.1038/242190a0
6 sg:pub.10.1038/ncomms13702
7 https://doi.org/10.1002/(sici)1098-1098(1999)10:3<216::aid-ima3>3.0.co;2-q
8 https://doi.org/10.1002/(sici)1522-2594(199911)42:5<952::aid-mrm16>3.0.co;2-s
9 https://doi.org/10.1002/1522-2594(200010)44:4<602::aid-mrm14>3.0.co;2-5
10 https://doi.org/10.1002/cmr.a.21243
11 https://doi.org/10.1002/jmri.20320
12 https://doi.org/10.1002/mrm.10171
13 https://doi.org/10.1002/mrm.1910380414
14 https://doi.org/10.1002/mrm.20819
15 https://doi.org/10.1002/mrm.21391
16 https://doi.org/10.1002/mrm.21643
17 https://doi.org/10.1002/mrm.22425
18 https://doi.org/10.1002/mrm.22463
19 https://doi.org/10.1002/mrm.22672
20 https://doi.org/10.1002/mrm.23146
21 https://doi.org/10.1002/mrm.24114
22 https://doi.org/10.1002/mrm.24115
23 https://doi.org/10.1002/mrm.24282
24 https://doi.org/10.1002/mrm.24443
25 https://doi.org/10.1002/mrm.24494
26 https://doi.org/10.1002/mrm.24928
27 https://doi.org/10.1002/mrm.25085
28 https://doi.org/10.1002/mrm.25152
29 https://doi.org/10.1002/mrm.25235
30 https://doi.org/10.1002/mrm.25347
31 https://doi.org/10.1002/mrm.25364
32 https://doi.org/10.1002/mrm.25423
33 https://doi.org/10.1002/mrm.25703
34 https://doi.org/10.1002/mrm.26145
35 https://doi.org/10.1002/mrm.26235
36 https://doi.org/10.1002/mrm.26700
37 https://doi.org/10.1016/j.jmr.2017.06.006
38 https://doi.org/10.1109/42.611345
39 https://doi.org/10.1109/icmipe.2013.6864568
40 https://doi.org/10.1109/isbi.2016.7493320
41 https://doi.org/10.1109/msp.2007.914728
42 https://doi.org/10.1109/tmi.1986.4307732
43 https://doi.org/10.1109/tmi.2017.2650960
44 https://doi.org/10.1109/tsp.2002.807005
45 https://doi.org/10.1259/0007-1285-50-591-188
46 schema:datePublished 2019-12
47 schema:datePublishedReg 2019-12-01
48 schema:description Fast ROtary Nonlinear Spatial ACquisition (FRONSAC) was recently introduced as a new strategy that applies nonlinear gradients as a small perturbation to improve image quality in highly undersampled MRI. In addition to experimentally showing the previously simulated improvement to image quality, this work introduces the insight that Cartesian-FRONSAC retains many desirable features of Cartesian imaging. Cartesian-FRONSAC preserves the existing linear gradient waveforms of the Cartesian sequence while adding oscillating nonlinear gradient waveforms. Experiments show that performance is essentially identical to Cartesian imaging in terms of (1) resilience to experimental imperfections, like timing errors or off-resonance spins, (2) accommodating scan geometry changes without the need for recalibration or additional field mapping, (3) contrast generation, as in turbo spin echo. Despite these similarities to Cartesian imaging, which provides poor parallel imaging performance, Cartesian-FRONSAC consistently shows reduced undersampling artifacts and better response to advanced reconstruction techniques. A final experiment shows that hardware requirements are also flexible. Cartesian-FRONSAC improves accelerated imaging while retaining the robustness and flexibility critical to real clinical use.
49 schema:genre research_article
50 schema:inLanguage en
51 schema:isAccessibleForFree true
52 schema:isPartOf N7a9984566b1c4b98a59b5dbd4a507821
53 N817156646df84444a09e7023cece189f
54 sg:journal.1045337
55 schema:name Clinical Potential of a New Approach to MRI Acceleration
56 schema:pagination 1912
57 schema:productId N183a2c7f5ecf486fa95585d4d2d83fc2
58 N2c31ebd489ab444cab86c95cdad9bcb3
59 Na1aea6c1c02d4da691944413d58c424d
60 Naa3c7647e8304159ab98f785a45a6e07
61 Ndb1cb5a7e5484f958e77d3b3a2f98107
62 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112094540
63 https://doi.org/10.1038/s41598-018-36802-5
64 schema:sdDatePublished 2019-04-11T13:18
65 schema:sdLicense https://scigraph.springernature.com/explorer/license/
66 schema:sdPublisher N8f7e2923aed641c3aa73301bdab58f3e
67 schema:url https://www.nature.com/articles/s41598-018-36802-5
68 sgo:license sg:explorer/license/
69 sgo:sdDataset articles
70 rdf:type schema:ScholarlyArticle
71 N0cff706e06e44a709b7717e8782ff92f rdf:first Nbbf7d23818f9498f958675af0a0aadea
72 rdf:rest N89ac7a9e67064fde9f5a6bd9a5458042
73 N1189fca60d0940efb3121941b1c4724d schema:affiliation https://www.grid.ac/institutes/grid.47100.32
74 schema:familyName Galiana
75 schema:givenName Gigi
76 rdf:type schema:Person
77 N183a2c7f5ecf486fa95585d4d2d83fc2 schema:name readcube_id
78 schema:value e72af1894651a249c9236a404dc236663a607729caf27faa1f6cc154a1967669
79 rdf:type schema:PropertyValue
80 N22793a96bcde4094b82bdddbadf4a509 rdf:first N1189fca60d0940efb3121941b1c4724d
81 rdf:rest rdf:nil
82 N2a3e370500854ce09cbfda0325bc8a41 rdf:first Nf2f8d821c7bd41dfb477d44e3d75c045
83 rdf:rest N22793a96bcde4094b82bdddbadf4a509
84 N2c31ebd489ab444cab86c95cdad9bcb3 schema:name pubmed_id
85 schema:value 30760731
86 rdf:type schema:PropertyValue
87 N7a9984566b1c4b98a59b5dbd4a507821 schema:volumeNumber 9
88 rdf:type schema:PublicationVolume
89 N817156646df84444a09e7023cece189f schema:issueNumber 1
90 rdf:type schema:PublicationIssue
91 N89ac7a9e67064fde9f5a6bd9a5458042 rdf:first Nb02764887c884c7f8319499abbdb97a6
92 rdf:rest Nf18ba99373964878ba0261c457821775
93 N8f7e2923aed641c3aa73301bdab58f3e schema:name Springer Nature - SN SciGraph project
94 rdf:type schema:Organization
95 Na1aea6c1c02d4da691944413d58c424d schema:name nlm_unique_id
96 schema:value 101563288
97 rdf:type schema:PropertyValue
98 Naa3c7647e8304159ab98f785a45a6e07 schema:name dimensions_id
99 schema:value pub.1112094540
100 rdf:type schema:PropertyValue
101 Nb02764887c884c7f8319499abbdb97a6 schema:affiliation https://www.grid.ac/institutes/grid.7708.8
102 schema:familyName Littin
103 schema:givenName Sebastian
104 rdf:type schema:Person
105 Nbbf7d23818f9498f958675af0a0aadea schema:affiliation https://www.grid.ac/institutes/grid.47100.32
106 schema:familyName Dispenza
107 schema:givenName Nadine L.
108 rdf:type schema:Person
109 Ndb1cb5a7e5484f958e77d3b3a2f98107 schema:name doi
110 schema:value 10.1038/s41598-018-36802-5
111 rdf:type schema:PropertyValue
112 Ne6caddcd77804bef9f7a8a2d7b3685c4 schema:affiliation https://www.grid.ac/institutes/grid.7708.8
113 schema:familyName Zaitsev
114 schema:givenName Maxim
115 rdf:type schema:Person
116 Nf18ba99373964878ba0261c457821775 rdf:first Ne6caddcd77804bef9f7a8a2d7b3685c4
117 rdf:rest N2a3e370500854ce09cbfda0325bc8a41
118 Nf2f8d821c7bd41dfb477d44e3d75c045 schema:affiliation https://www.grid.ac/institutes/grid.47100.32
119 schema:familyName Constable
120 schema:givenName R. Todd
121 rdf:type schema:Person
122 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
123 schema:name Physical Sciences
124 rdf:type schema:DefinedTerm
125 anzsrc-for:0299 schema:inDefinedTermSet anzsrc-for:
126 schema:name Other Physical Sciences
127 rdf:type schema:DefinedTerm
128 sg:grant.2502018 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-018-36802-5
129 rdf:type schema:MonetaryGrant
130 sg:grant.2502169 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-018-36802-5
131 rdf:type schema:MonetaryGrant
132 sg:grant.5475983 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-018-36802-5
133 rdf:type schema:MonetaryGrant
134 sg:grant.7071947 http://pending.schema.org/fundedItem sg:pub.10.1038/s41598-018-36802-5
135 rdf:type schema:MonetaryGrant
136 sg:journal.1045337 schema:issn 2045-2322
137 schema:name Scientific Reports
138 rdf:type schema:Periodical
139 sg:pub.10.1007/s10334-008-0105-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034760603
140 https://doi.org/10.1007/s10334-008-0105-7
141 rdf:type schema:CreativeWork
142 sg:pub.10.1038/242190a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031397834
143 https://doi.org/10.1038/242190a0
144 rdf:type schema:CreativeWork
145 sg:pub.10.1038/ncomms13702 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023322000
146 https://doi.org/10.1038/ncomms13702
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1002/(sici)1098-1098(1999)10:3<216::aid-ima3>3.0.co;2-q schema:sameAs https://app.dimensions.ai/details/publication/pub.1036803680
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1002/(sici)1522-2594(199911)42:5<952::aid-mrm16>3.0.co;2-s schema:sameAs https://app.dimensions.ai/details/publication/pub.1029323697
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1002/1522-2594(200010)44:4<602::aid-mrm14>3.0.co;2-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025405172
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1002/cmr.a.21243 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038541259
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1002/jmri.20320 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002650411
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1002/mrm.10171 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012449961
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1002/mrm.1910380414 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018509237
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1002/mrm.20819 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000131680
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1002/mrm.21391 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037838340
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1002/mrm.21643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000647026
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1002/mrm.22425 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035737847
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1002/mrm.22463 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004356511
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1002/mrm.22672 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007226138
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1002/mrm.23146 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035044168
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1002/mrm.24114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037910933
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1002/mrm.24115 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026454831
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1002/mrm.24282 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014922979
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1002/mrm.24443 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025997937
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1002/mrm.24494 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009345523
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1002/mrm.24928 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030259736
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1002/mrm.25085 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049261401
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1002/mrm.25152 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006183265
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1002/mrm.25235 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041918058
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1002/mrm.25347 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023308255
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1002/mrm.25364 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009061597
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1002/mrm.25423 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030101481
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1002/mrm.25703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039826229
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1002/mrm.26145 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006066834
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1002/mrm.26235 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015424945
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1002/mrm.26700 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085058132
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1016/j.jmr.2017.06.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085962623
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1109/42.611345 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061170548
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1109/icmipe.2013.6864568 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094261026
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1109/isbi.2016.7493320 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095595792
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1109/msp.2007.914728 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061422951
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1109/tmi.1986.4307732 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061694123
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1109/tmi.2017.2650960 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061696872
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1109/tsp.2002.807005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061798816
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1259/0007-1285-50-591-188 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064551653
225 rdf:type schema:CreativeWork
226 https://www.grid.ac/institutes/grid.47100.32 schema:alternateName Yale University
227 schema:name Department of Biomedical Engineering, Yale University, New Haven, CT, USA
228 Department of Neurosurgery, Yale University, 06520, New Haven, CT, USA
229 Department of Radiology and Biomedical Imaging, Yale University, 06520, New Haven, CT, USA
230 rdf:type schema:Organization
231 https://www.grid.ac/institutes/grid.7708.8 schema:alternateName University Medical Center Freiburg
232 schema:name Department of Diagnostic Radiology, Medical Physics, University Medical Center Freiburg, Breisacher Str. 60a, 79106, Freiburg, Germany
233 rdf:type schema:Organization
 




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


...