Deep Learning Interior Tomography for Region-of-Interest Reconstruction
This addresses the issue of high computational cost and artifacts in ROI imaging for medical CT applications, offering a more efficient solution.
The paper tackled the problem of cupping artifacts in interior tomography for region-of-interest reconstruction by proposing a deep learning architecture that removes null space signals from FBP reconstruction, resulting in near-perfect reconstruction with a 7-10 dB PSNR improvement over existing methods.
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.