  Sparse-view computed tomography (CT) has been adopted as an important
technique for speeding up data acquisition and decreasing radiation dose.
However, due to the lack of sufficient projection data, the reconstructed CT
images often present severe artifacts, which will be further amplified when
patients carry metallic implants. For this joint sparse-view reconstruction and
metal artifact reduction task, most of the existing methods are generally
confronted with two main limitations: 1) They are almost built based on common
network modules without fully embedding the physical imaging geometry
constraint of this specific task into the dual-domain learning; 2) Some
important prior knowledge is not deeply explored and sufficiently utilized.
Against these issues, we specifically construct a dual-domain reconstruction
model and propose a model-driven equivariant proximal network, called MEPNet.
The main characteristics of MEPNet are: 1) It is optimization-inspired and has
a clear working mechanism; 2) The involved proximal operator is modeled via a
rotation equivariant convolutional neural network, which finely represents the
inherent rotational prior underlying the CT scanning that the same organ can be
imaged at different angles. Extensive experiments conducted on several datasets
comprehensively substantiate that compared with the conventional
convolution-based proximal network, such a rotation equivariance mechanism
enables our proposed method to achieve better reconstruction performance with
fewer network parameters. We will release the code at
\url{https://github.com/hongwang01/MEPNet}.
