  Classic algebraic reconstruction technology (ART) for computed tomography
requires pre-determined weights of the voxels for projecting pixel values.
However, such weight cannot be accurately obtained due to the limitation of the
physical understanding and computation resources. In this study, we propose a
semi-case-wise learning-based method named Weight Encode Reconstruction Network
(WERNet) to tackle the issues mentioned above. The model is trained in a
self-supervised manner without the label of a voxel set. It contains two
branches, including the voxel weight encoder and the voxel attention part.
Using gradient normalization, we are able to co-train the encoder and voxel set
numerically stably. With WERNet, the reconstructed result was obtained with a
cosine similarity greater than 0.999 with the ground truth. Moreover, the model
shows the extraordinary capability of denoising comparing to the classic ART
method. In the generalization test of the model, the encoder is transferable
from a voxel set with complex structure to the unseen cases without the
deduction of the accuracy.
