Weight Encode Reconstruction Network for Computed Tomography in a Semi-Case-Wise and Learning-Based Way

@article{Pan2020WeightER,
  title={Weight Encode Reconstruction Network for Computed Tomography in a Semi-Case-Wise and Learning-Based Way},
  author={Hujie Pan and Xuesong Li and Min Xu},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.00893}
}
Classic algebraic reconstruction technology (ART) for computed tomography requires pre-determined weights of the voxels for the projected pixel values to build the equations. However, such weights cannot be accurately obtained due to the high physical complexity and computation resources required. In this study, we propose a semi-case-wise learning-based method named Weight Encode Reconstruction Network (WERNet) to co-learn the target voxel values and intrinsic physics of the case in a self… 

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