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    <title language="eng">Geometry of Deep Learning for Magnetic Resonance Fingerprinting</title>
    <parentTitle language="eng">ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</parentTitle>
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    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
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    <author>
      <first_name>Dongdong</first_name>
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      <first_name>Pedro A.</first_name>
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      <first_name>Marion Irene</first_name>
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      <last_name>Davies</last_name>
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      <language>eng</language>
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      <value>magnetic resonance fingerprinting</value>
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    <subject>
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      <value>inverse problem</value>
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      <value>manifold compressed sensing</value>
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    <language>eng</language>
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    <title language="eng">Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting</title>
    <parentTitle language="eng">Proceedings of Machine Learning Research</parentTitle>
    <identifier type="issn">2640-3498</identifier>
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      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
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    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Ilona</first_name>
      <last_name>Lipp</last_name>
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    <author>
      <first_name>Guido</first_name>
      <last_name>Buonincontri</last_name>
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    <author>
      <first_name>Miguel</first_name>
      <last_name>Molina-Romero</last_name>
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    <author>
      <first_name>Anjany</first_name>
      <last_name>Sekuboyina</last_name>
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    <author>
      <first_name>Diana</first_name>
      <last_name>Waldmannstetter</last_name>
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    <author>
      <first_name>Jonathan</first_name>
      <last_name>Dannenberg</last_name>
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    <author>
      <first_name>Sebastian</first_name>
      <last_name>Endt</last_name>
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    <author>
      <first_name>Alberto</first_name>
      <last_name>Merola</last_name>
    </author>
    <author>
      <first_name>Joseph R.</first_name>
      <last_name>Whittaker</last_name>
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    <author>
      <first_name>Valentina</first_name>
      <last_name>Tomassini</last_name>
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    <author>
      <first_name>Michela</first_name>
      <last_name>Tosetti</last_name>
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    <author>
      <first_name>Derek K.</first_name>
      <last_name>Jones</last_name>
    </author>
    <author>
      <first_name>Bjoern H.</first_name>
      <last_name>Menze</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetic Resonance Fingerprinting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Convolutional Neural Network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image Reconstruction</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Diffusion Tensor</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiple Sclerosis</value>
    </subject>
    <collection role="persons" number="44549">Menzel, Marion</collection>
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  <doc>
    <id>2822</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
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    <language>eng</language>
    <pageFirst>98</pageFirst>
    <pageLast>106</pageLast>
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    <publisherName>Springer</publisherName>
    <publisherPlace>Cham</publisherPlace>
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    <completedDate>2022-09-06</completedDate>
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    <title language="eng">Deep Learning with Synthetic Diffusion MRI Data for Free-Water Elimination in Glioblastoma Cases</title>
    <parentTitle language="eng">Medical Image Computing and Computer Assisted Intervention – MICCAI 2018</parentTitle>
    <identifier type="isbn">978-3-030-00931-1</identifier>
    <identifier type="isbn">978-3-030-00930-4</identifier>
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    <author>
      <first_name>Miguel</first_name>
      <last_name>Molina-Romero</last_name>
    </author>
    <author>
      <first_name>Benedikt</first_name>
      <last_name>Wiestler</last_name>
    </author>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
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    <author>
      <first_name>Bjoern H.</first_name>
      <last_name>Menze</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Glioblastoma</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Brain tumor</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>DTI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Free-water elimination</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data harmonization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fractional anisotropy</value>
    </subject>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>3116</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>4</pageNumber>
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    <title language="eng">Deep Fully Convolutional Network for MR Fingerprinting</title>
    <abstract language="eng">This work proposes an end-to-end deep fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employs linear dimensionality reduction and then uses a neural network to project the data into the tissue parameters. The MRF dictionary is only used for training the network and not during image reconstruction.  We show that MRF-FCNN is capable of achieving accuracy comparable to the ground-truth maps thanks to capturing spatio-temporal data structures without a need for the non-scalable dictionary matching step used in the baseline reconstructions.</abstract>
    <parentTitle language="eng">Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track</parentTitle>
    <identifier type="url">https://openreview.net/forum?id=SJxUdvJTtN</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-31163</identifier>
    <enrichment key="THI_conferenceName">Medical Imaging with Deep Learning (MIDL 2019), London (UK), 08.-10.07.2019</enrichment>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Dongdong</first_name>
      <last_name>Chen</last_name>
    </author>
    <editor>
      <first_name>M. Jorge</first_name>
      <last_name>Cardoso</last_name>
    </editor>
    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
    </author>
    <editor>
      <first_name>Aasa</first_name>
      <last_name>Feragen</last_name>
    </editor>
    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
    </author>
    <editor>
      <first_name>Ben</first_name>
      <last_name>Glocker</last_name>
    </editor>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <editor>
      <first_name>Ender</first_name>
      <last_name>Konukoglu</last_name>
    </editor>
    <author>
      <first_name>Mike E.</first_name>
      <last_name>Davies</last_name>
    </author>
    <editor>
      <first_name>Ipek</first_name>
      <last_name>Oguz</last_name>
    </editor>
    <editor>
      <first_name>Gozde</first_name>
      <last_name>Unal</last_name>
    </editor>
    <editor>
      <first_name>Tom</first_name>
      <last_name>Vercauteren</last_name>
    </editor>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetic Resonance Fingerprinting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3116/28_deep_fully_convolutional_netwo.pdf</file>
  </doc>
  <doc>
    <id>3117</id>
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    <publishedYear>2019</publishedYear>
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    <language>eng</language>
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    <pageLast/>
    <pageNumber>4</pageNumber>
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    <title language="eng">Spatio-temporal regularization for deep MR Fingerprinting</title>
    <abstract language="eng">We study a deep learning approach to address the heavy storage and computation re- quirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprint- ing (MRF) reconstruction. The MRF-Net provides a piece-wise affine approximation to the (temporal) Bloch response manifold projection. Fed with non-iterated back-projected images, the network alone is unable to fully resolve spatially-correlated artefacts which ap- pear in highly undersampling regimes. We propose an accelerated iterative reconstruction to minimize these artefacts before feeding into the network. This is done through a convex regularization that jointly promotes spatio-temporal regularities of the MRF time-series.</abstract>
    <parentTitle language="eng">Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track</parentTitle>
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    <identifier type="url">https://2019.midl.io/program/extended-abstracts.html</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-31174</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Mohammad</first_name>
      <last_name>Golbabaee</last_name>
    </author>
    <editor>
      <first_name>M. Jorge</first_name>
      <last_name>Cardoso</last_name>
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    <author>
      <first_name>Dongdong</first_name>
      <last_name>Chen</last_name>
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    <editor>
      <first_name>Aasa</first_name>
      <last_name>Feragen</last_name>
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    <author>
      <first_name>Mike E.</first_name>
      <last_name>Davies</last_name>
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    <editor>
      <first_name>Ben</first_name>
      <last_name>Glocker</last_name>
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    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
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    <editor>
      <first_name>Ender</first_name>
      <last_name>Konukoglu</last_name>
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    <author>
      <first_name>Pedro A.</first_name>
      <last_name>Gómez</last_name>
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    <editor>
      <first_name>Ipek</first_name>
      <last_name>Oguz</last_name>
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    <editor>
      <first_name>Gozde</first_name>
      <last_name>Unal</last_name>
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      <first_name>Tom</first_name>
      <last_name>Vercauteren</last_name>
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      <language>eng</language>
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      <value>magnetic resonance fingerprinting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep learning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>regularisation</value>
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    <publishedYear>2015</publishedYear>
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    <title language="eng">Joint Reconstruction of Multi-Contrast MRI for Multiple Sclerosis Lesion Segmentation</title>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2015, Algorithmen – Systeme – Anwendungen, Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck</parentTitle>
    <identifier type="isbn">978-3-662-46224-9</identifier>
    <identifier type="isbn">978-3-662-46223-2</identifier>
    <note>Die vollständige Angabe der Autorinnen und Autoren findet sich im PDF.</note>
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