abstract—interior tomography for the region-of-interest imaging has advantages of using a small detector and reducing x-ray
radiation dose. however, standard analytic reconstruction suffers
from severe cupping artifacts due to existence of null space in the
truncated radon transform. existing penalized reconstruction
methods may address this problem but they require extensive
computations due to the iterative reconstruction. inspired by the
recent deep learning approaches to low-dose and sparse view
ct, here we propose a deep learning architecture that removes
null space signals from the fbp reconstruction. experimental
results have shown that the proposed method provides nearperfect reconstruction with about 7 ∼ 10db improvement in
psnr over existing methods in spite of significantly reduced
run-time complexity.