Title: Deep Learning Interior Tomography for Region-of-Interest Reconstruction,
Abstract: Interior tomography for the region-of-interest (ROI) 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 near-perfect reconstruction with about 7-10 dB improvement in PSNR
over existing methods in spite of significantly reduced run-time complexity.