A data consistent artifact reduction method that couples a U-Net prior with iterative reconstruction reduces RMSE by over 10% (noise-free) and over 24% (noisy) in simulated 120 degree cone-beam limited angle tomography.
A deep convolutional neural network using di- rectional wavelets for low-dose X-ray CT reconstruction,
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Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior
A data consistent artifact reduction method that couples a U-Net prior with iterative reconstruction reduces RMSE by over 10% (noise-free) and over 24% (noisy) in simulated 120 degree cone-beam limited angle tomography.