  X-ray Computed Tomography (CT) is widely used in clinical applications such
as diagnosis and image-guided interventions. In this paper, we propose a new
deep learning based model for CT image reconstruction with the backbone network
architecture built by unrolling an iterative algorithm. However, unlike the
existing strategy to include as many data-adaptive components in the unrolled
dynamics model as possible, we find that it is enough to only learn the parts
where traditional designs mostly rely on intuitions and experience. More
specifically, we propose to learn an initializer for the conjugate gradient
(CG) algorithm that involved in one of the subproblems of the backbone model.
Other components, such as image priors and hyperparameters, are kept as the
original design. Since a hypernetwork is introduced to inference on the
initialization of the CG module, it makes the proposed model a certain
meta-learning model. Therefore, we shall call the proposed model the
meta-inversion network (MetaInv-Net). The proposed MetaInv-Net can be designed
with much less trainable parameters while still preserves its superior image
reconstruction performance than some state-of-the-art deep models in CT
imaging. In simulated and real data experiments, MetaInv-Net performs very well
and can be generalized beyond the training setting, i.e., to other scanning
settings, noise levels, and data sets.
