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@ARTICLE{Pfaehler:1033914,
      author       = {Pfaehler, Elisabeth and Pflugfelder, Daniel and Scharr,
                      Hanno},
      title        = {{U}ntrained {P}erceptual {L}oss for image denoising of
                      line-like structures in {MR} images},
      reportid     = {FZJ-2024-06752, arXiv:2411.05884},
      year         = {2024},
      abstract     = {In the acquisition of Magnetic Resonance (MR) images
                      shorter scan times lead to higher image noise. Therefore,
                      automatic image denoising using deep learning methods is of
                      high interest. MR images containing line-like structures
                      such as roots or vessels yield special characteristics as
                      they display connected structures and yield sparse
                      information. For this kind of data, it is important to
                      consider voxel neighborhoods when training a denoising
                      network. In this paper, we translate the Perceptual Loss to
                      3D data by comparing feature maps of untrained networks in
                      the loss function as done previously for 2D data. We tested
                      the performance of untrained Perceptual Loss (uPL) on 3D
                      image denoising of MR images displaying brain vessels (MR
                      angiograms - MRA) and images of plant roots in soil. We
                      investigate the impact of various uPL characteristics such
                      as weight initialization, network depth, kernel size, and
                      pooling operations on the results. We tested the performance
                      of the uPL loss on four Rician noise levels using evaluation
                      metrics such as the Structural Similarity Index Metric
                      (SSIM). We observe, that our uPL outperforms conventional
                      loss functions such as the L1 loss or a loss based on the
                      Structural Similarity Index Metric (SSIM). The uPL network's
                      initialization is not important, while network depth and
                      pooling operations impact denoising performance. E.g. for
                      both datasets a network with five convolutional layers led
                      to the best performance while a network with more layers led
                      to a performance drop. We also find that small uPL networks
                      led to better or comparable results than using large
                      networks such as VGG. We observe superior performance of our
                      loss for both datasets, all noise levels, and three network
                      architectures. In conclusion, for images containing
                      line-like structures, uPL is an alternative to other loss
                      functions for 3D image denoising.},
      cin          = {IAS-8},
      cid          = {I:(DE-Juel1)IAS-8-20210421},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2411.05884},
      howpublished = {arXiv:2411.05884},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2411.05884;\%\%$},
      url          = {https://juser.fz-juelich.de/record/1033914},
}