Name
Abstract | ||
|---|---|---|
Model-based iterative reconstruction (MBIR) is increasingly widely applied as an improvement over conventional, deterministic methods of image reconstruction in X-ray CT. A primary advantage of MBIR is potentially drastically reduced dosage without diagnostic quality loss. Early success of the method has naturally led to growing numbers of scans at very low dose, presenting data which does not match well the simple statistical models heretofore considered adequate. This paper addresses several issues arising in limiting cases which call for refinement of standard data models. The emergence of electronic noise as a significant contributor to uncertainty, and bias of sinogram values in photon-starved measurements are demonstrated to be important modeling problems in this new environment. We present also possible ameliorations to several of these low-dosage estimation issues. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1117/12.2013231 | COMPUTATIONAL IMAGING XI |
Keywords | Field | DocType |
CT reconstruction, Bayesian estimation, beam hardening, photon starvation, Poisson statistics | Iterative reconstruction,Computer vision,Data modeling,Noise (electronics),Computed tomography,Statistical model,Artificial intelligence,Image restoration,Limiting,Physics | Conference |
Volume | ISSN | Citations |
8657 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruoqiao Zhang | 1 | 24 | 2.71 |
| Aaron Chang | 2 | 0 | 0.34 |
| Jean-Baptiste Thibault | 3 | 40 | 6.78 |
| Ken D. Sauer | 4 | 576 | 90.54 |
| Charles A. Bouman | 5 | 2740 | 473.62 |