  Breast CT provides image volumes with isotropic resolution in high contrast,
enabling detection of small calcification (down to a few hundred microns in
size) and subtle density differences. Since breast is sensitive to x-ray
radiation, dose reduction of breast CT is an important topic, and for this
purpose, few-view scanning is a main approach. In this article, we propose a
Deep Efficient End-to-end Reconstruction (DEER) network for few-view breast CT
image reconstruction. The major merits of our network include high dose
efficiency, excellent image quality, and low model complexity. By the design,
the proposed network can learn the reconstruction process with as few as O(N)
parameters, where N is the side length of an image to be reconstructed, which
represents orders of magnitude improvements relative to the state-of-the-art
deep-learning-based reconstruction methods that map raw data to tomographic
images directly. Also, validated on a cone-beam breast CT dataset prepared by
Koning Corporation on a commercial scanner, our method demonstrates a
competitive performance over the state-of-the-art reconstruction networks in
terms of image quality. The source code of this paper is available at:
https://github.com/HuidongXie/DEER.
