Name
Abstract | ||
|---|---|---|
This work presents a novel computed tomography reconstruction method for few-view problem based on a compound method. To overcome the disadvantages of total variation (TV) minimization method, we use a high-order norm coupled within TV and the numerical scheme for our method is given. We use the root mean square error as a referee. The numerical experiments demonstrate that our method achieves better performance than existing reconstruction methods, including filtered back projection, expectation maximization, and TV with projection on convex sets. (c) 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 249-255, 2013 |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1002/ima.22058 | INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY |
Keywords | Field | DocType |
x-ray computed tomography, few-view, total variation, image reconstruction | Computer science,Mean squared error,Convex set,Projection method,Minimisation (psychology),Minification,Artificial intelligence,Radon transform,Iterative reconstruction,Computer vision,Expectation–maximization algorithm,Algorithm,Calculus | Journal |
Volume | Issue | ISSN |
23 | 3 | 0899-9457 |
Citations | PageRank | References |
6 | 0.89 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhang Yi | 1 | 356 | 37.14 |
| Wei-hua Zhang | 2 | 18 | 1.64 |
| Hu Chen | 3 | 13 | 3.75 |
| Menglong Yang | 4 | 109 | 10.49 |
| Taiyong Li | 5 | 93 | 9.44 |
| Jiliu Zhou | 6 | 450 | 58.21 |