Journal article
Interior Reconstruction Using the Truncated Hilbert Transform via Singular Value Decomposition
Journal of X-ray science and technology, Vol.16(4), pp.243-251
01/01/2008
PMCID: PMC2860313
PMID: 20428482
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.
Details
- Title: Subtitle
- Interior Reconstruction Using the Truncated Hilbert Transform via Singular Value Decomposition
- Creators
- Hengyong Yu - University of IowaYangbo Ye - CT Laboratory, Biomedical Imaging Division, VT-WFU School of Biomedical Engineering Virginia Tech, Blacksburg, VA 24061, USA Department of Mathematics, University of Iowa, Iowa City, Iowa 52242, USAGe Wang - CT Laboratory, Biomedical Imaging Division, VT-WFU School of Biomedical Engineering Virginia Tech, Blacksburg, VA 24061, USA Department of Mathematics, University of Iowa, Iowa City, Iowa 52242, USA
- Resource Type
- Journal article
- Publication Details
- Journal of X-ray science and technology, Vol.16(4), pp.243-251
- PMID
- 20428482
- PMCID
- PMC2860313
- ISSN
- 0895-3996
- eISSN
- 1095-9114
- Language
- English
- Date published
- 01/01/2008
- Academic Unit
- Mathematics
- Record Identifier
- 9984240765802771
Metrics
24 Record Views