Ultra-Sparse View Reconstruction for Flash X-Ray Imaging Using Consensus Equilibrium
| Autor: | Shane Paulson, Hangjie Liao, Maliha Hossain, Weinong Chen, Charles A. Bouman |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Iterative method
Computer science Bar (music) Generalization business.industry Image quality Image and Video Processing (eess.IV) 02 engineering and technology Solid modeling Iterative reconstruction Electrical Engineering and Systems Science - Image and Video Processing Object (computer science) 01 natural sciences 010309 optics Flash (photography) 0103 physical sciences FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | ACSSC |
| DOI: | 10.1109/ieeeconf51394.2020.9443350 |
| Popis: | 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. Comment: To be published in Asilomar Conference on Signals, Systems, and Computers 2020 |
| Databáze: | OpenAIRE |
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