2017/12/29 by Yoseob Han, Han, Yoseob, Jawook Gu +3 · 1 citation
Medicine · Physics and Astronomy · #Medical Imaging Techniques and Applications #Advanced X-ray Imaging Techniques #Advanced MRI Techniques and Applications
paper · pdf · doi:10.48550/arxiv.1712.10248
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.