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Article type: Research Article
Authors: Yu, Hengyong | Ye, Yangbo | Wang, Ge
Affiliations: CT Laboratory, Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA | Department of Mathematics, University of Iowa, Iowa City, IA, USA
Note: [] Corresponding author. E-mail: hengyong-yu@ieee.org
Abstract: The state-of-the-art technology for theoretically exact local computed tomography (CT) is to reconstruct an object function using the truncated Hilbert transform (THT) via the projection onto convex sets (POCS) method, which is iterative and computationally expensive. Here we propose to reconstruct the object function using the THT via singular value decomposition (SVD). First, we review the major steps of our algorithm. Then, we implement the proposed SVD method and perform numerical simulations. Our numerical results indicate that our approach runs two orders of magnitude faster than the iterative approach and produces an excellent region-of-interest (ROI) reconstruction that was previously impossible, demonstrating the feasibility of localized pre-clinical and clinical CT as a new direction for research on exact local image reconstruction. Finally, relevant issues are discussed.
Keywords: Computed tomography (CT), truncated Hilbert transform (THT), singular value decomposition (SVD), regularization, local image reconstruction
Journal: Journal of X-Ray Science and Technology, vol. 16, no. 4, pp. 243-251, 2008
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