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@ARTICLE{Pawar:868442,
      author       = {Pawar, Kamlesh and Chen, Zhaolin and Shah, N. J. and Egan,
                      Gary. F.},
      title        = {{A} {D}eep {L}earning {F}ramework for {T}ransforming
                      {I}mage {R}econstruction {I}nto {P}ixel {C}lassification},
      journal      = {IEEE access},
      volume       = {7},
      issn         = {2169-3536},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2020-00036},
      pages        = {177690 - 177702},
      year         = {2019},
      abstract     = {A deep learning framework is presented that transforms the
                      task of MR image reconstruction from randomly undersampled
                      k-space data into pixel classification. A DL network was
                      trained to remove incoherent undersampling artifacts from MR
                      images. The underlying, fully sampled, target image was
                      represented as a discrete quantized image. The quantization
                      step enables the design of a convolutional neural network
                      (CNN) that can classify each pixel in the input image to a
                      discrete quantized level. The reconstructed image quality of
                      the proposed DL classification model was compared with
                      conventional compressed sensing (CS) and a DL regression
                      model. The reconstructed images using the DL classification
                      model outperformed the state-of-the-art compressed sensing
                      and DL regression models with a similar number of parameters
                      assessed using quantitative measures. The experiments reveal
                      that the proposed deep learning method is robust to noise
                      and is able to reconstruct high-quality images in low SNR
                      scenarios where conventional CS reconstructions and DL
                      regression networks perform poorly. A generic design
                      framework for transforming MR image reconstruction into
                      pixel classification is developed. The proposed method can
                      be easily incorporated into other DL-based image
                      reconstruction methods.},
      cin          = {INM-4},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)INM-4-20090406},
      pnm          = {573 - Neuroimaging (POF3-573)},
      pid          = {G:(DE-HGF)POF3-573},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000509483800029},
      doi          = {10.1109/ACCESS.2019.2959037},
      url          = {https://juser.fz-juelich.de/record/868442},
}