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@ARTICLE{Pawar:908218,
      author       = {Pawar, Kamlesh and Chen, Zhaolin and Shah, N. J. and Egan,
                      Gary F.},
      title        = {{S}uppressing motion artefacts in {MRI} using an
                      {I}nception‐{R}es{N}et network with motion simulation
                      augmentation},
      journal      = {NMR in biomedicine},
      volume       = {35},
      number       = {4},
      issn         = {0952-3480},
      address      = {New York, NY},
      publisher    = {Wiley},
      reportid     = {FZJ-2022-02469},
      pages        = {e4225},
      year         = {2022},
      abstract     = {The suppression of motion artefacts from MR images is a
                      challenging task. The purpose of this paper was to develop a
                      standalone novel technique to suppress motion artefacts in
                      MR images using a data-driven deep learning approach. A
                      simulation framework was developed to generate
                      motion-corrupted images from motion-free images using
                      randomly generated motion profiles. An Inception-ResNet deep
                      learning network architecture was used as the encoder and
                      was augmented with a stack of convolution and upsampling
                      layers to form an encoder-decoder network. The network was
                      trained on simulated motion-corrupted images to identify and
                      suppress those artefacts attributable to motion. The network
                      was validated on unseen simulated datasets and real-world
                      experimental motion-corrupted in vivo brain datasets. The
                      trained network was able to suppress the motion artefacts in
                      the reconstructed images, and the mean structural similarity
                      (SSIM) increased from 0.9058 to 0.9338. The network was also
                      able to suppress the motion artefacts from the real-world
                      experimental dataset, and the mean SSIM increased from
                      0.8671 to 0.9145. The motion correction of the experimental
                      datasets demonstrated the effectiveness of the motion
                      simulation generation process. The proposed method
                      successfully removed motion artefacts and outperformed an
                      iterative entropy minimization method in terms of the SSIM
                      index and normalized root mean squared error, which were
                      $5–10\%$ better for the proposed method. In conclusion, a
                      novel, data-driven motion correction technique has been
                      developed that can suppress motion artefacts from
                      motion-corrupted MR images. The proposed technique is a
                      standalone, post-processing method that does not interfere
                      with data acquisition or reconstruction parameters, thus
                      making it suitable for routine clinical practice.},
      cin          = {INM-4 / INM-11 / JARA-BRAIN},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      I:(DE-Juel1)VDB1046},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31865624},
      UT           = {WOS:000503702600001},
      doi          = {10.1002/nbm.4225},
      url          = {https://juser.fz-juelich.de/record/908218},
}