Name
Abstract | ||
|---|---|---|
A growing number of applications require the reconstructionof 3D objects from a very small number of views. In this research, we consider the problem of reconstructing a 3D object from only 4 Flash X-ray CT views taken during the impact of a Kolsky bar. For such ultra-sparse view datasets, even model-based iterative reconstruction (MBIR) methods produce poor quality results. In this paper, we present a framework based on a generalization of Plug-and-Play, known as Multi-Agent Consensus Equilibrium (MACE), for incorporating complex and nonlinear prior information into ultra-sparse CT reconstruction. The MACE method allows any number of agents to simultaneously enforce their own prior constraints on the solution. We apply our method on simulated and real data and demonstrate that MACE reduces artifacts, improves reconstructed image quality, and uncovers image features which were otherwise indiscernible. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IEEECONF51394.2020.9443350 | ACSSC |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maliha Hossain | 1 | 0 | 0.34 |
| Shane C. Paulson | 2 | 0 | 0.34 |
| Hangjie Liao | 3 | 0 | 0.34 |
| Wienong W. Chen | 4 | 0 | 0.34 |
| Charles A. Bouman | 5 | 2740 | 473.62 |