  Interior tomography is a typical strategy for radiation dose reduction in
computed tomography, where only a certain region-of-interest (ROI) is scanned.
However, given the truncated projection data, ROI reconstruction by
conventional analytical algorithms may suffer from severe cupping artifacts. In
this paper, we proposed a new extraction-based deep learning method for the
reconstruction of interior tomography. Our approach works in dual domains where
a sinogram-domain network (SDNet) estimates the contribution of the exterior
region to the truncated projection and an image-domain network (IDNet) further
mitigates artifacts. Unlike the previous extrapolation-based methods, SDNet is
intended to obtain a complete ROI-only sinogram via extraction instead of a
fully non-truncated sinogram for both the ROI and exterior regions. Our
experiments validated the proposed method and the results indicate that the
proposed method can disclose more reliable structures. It achieved better image
quality with better generalization performance than extrapolation-based
methods.
