SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network

Steffen Bollmann, Matilde Holm Kristensen, Morten Skaarup Larsen, Mathias Vassard Olsen, Mads Jozwiak Pedersen, Lasse Riis Østergaard, Kieran O'Brien, Christian Langkammer, Amir Fazlollahi, Markus Barth

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1 Citation (Scopus)
OriginalsprogEngelsk
TidsskriftMedical Physics
Vol/bind29
Udgave nummer2
Sider (fra-til)139-149
Antal sider11
ISSN0094-2405
DOI
StatusUdgivet - 1 maj 2019

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Copyright © 2019. Published by Elsevier GmbH.

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    Bollmann, Steffen ; Kristensen, Matilde Holm ; Larsen, Morten Skaarup ; Olsen, Mathias Vassard ; Pedersen, Mads Jozwiak ; Østergaard, Lasse Riis ; O'Brien, Kieran ; Langkammer, Christian ; Fazlollahi, Amir ; Barth, Markus. / SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network. I: Medical Physics. 2019 ; Bind 29, Nr. 2. s. 139-149.
    @article{bcc87912a88a4e1db74c333ca357e474,
    title = "SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network",
    keywords = "Background field correction, Deep learning, Quantitative susceptibility mapping",
    author = "Steffen Bollmann and Kristensen, {Matilde Holm} and Larsen, {Morten Skaarup} and Olsen, {Mathias Vassard} and Pedersen, {Mads Jozwiak} and {\O}stergaard, {Lasse Riis} and Kieran O'Brien and Christian Langkammer and Amir Fazlollahi and Markus Barth",
    note = "Copyright {\circledC} 2019. Published by Elsevier GmbH.",
    year = "2019",
    month = "5",
    day = "1",
    doi = "10.1016/j.zemedi.2019.01.001",
    language = "English",
    volume = "29",
    pages = "139--149",
    journal = "Medical Physics",
    issn = "0094-2405",
    publisher = "John Wiley and Sons, Inc.",
    number = "2",

    }

    Bollmann, S, Kristensen, MH, Larsen, MS, Olsen, MV, Pedersen, MJ, Østergaard, LR, O'Brien, K, Langkammer, C, Fazlollahi, A & Barth, M 2019, 'SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network' Medical Physics, bind 29, nr. 2, s. 139-149. https://doi.org/10.1016/j.zemedi.2019.01.001

    SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network. / Bollmann, Steffen; Kristensen, Matilde Holm; Larsen, Morten Skaarup; Olsen, Mathias Vassard; Pedersen, Mads Jozwiak; Østergaard, Lasse Riis; O'Brien, Kieran; Langkammer, Christian; Fazlollahi, Amir; Barth, Markus.

    I: Medical Physics, Bind 29, Nr. 2, 01.05.2019, s. 139-149.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    TY - JOUR

    T1 - SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network

    AU - Bollmann, Steffen

    AU - Kristensen, Matilde Holm

    AU - Larsen, Morten Skaarup

    AU - Olsen, Mathias Vassard

    AU - Pedersen, Mads Jozwiak

    AU - Østergaard, Lasse Riis

    AU - O'Brien, Kieran

    AU - Langkammer, Christian

    AU - Fazlollahi, Amir

    AU - Barth, Markus

    N1 - Copyright © 2019. Published by Elsevier GmbH.

    PY - 2019/5/1

    Y1 - 2019/5/1

    KW - Background field correction

    KW - Deep learning

    KW - Quantitative susceptibility mapping

    UR - http://www.scopus.com/inward/record.url?scp=85061458101&partnerID=8YFLogxK

    U2 - 10.1016/j.zemedi.2019.01.001

    DO - 10.1016/j.zemedi.2019.01.001

    M3 - Journal article

    VL - 29

    SP - 139

    EP - 149

    JO - Medical Physics

    JF - Medical Physics

    SN - 0094-2405

    IS - 2

    ER -