Paper Info
Title
CodEx: A Modular Framework for Joint Temporal De-Blurring and Tomographic Reconstruction
Abstract
In many computed tomography (CT) imaging applications, it is important to rapidly collect data from an object that is moving or changing with time. Tomographic acquisition is generally assumed to be step-and-shoot, where the object is rotated to each desired angle, and a view is taken. However, step-and-shoot acquisition is slow and can waste photons, so in practice fly-scanning is done where the object is continuously rotated while collecting data. However, this can result in motion-blurred views and consequently reconstructions with severe motion artifacts. In this paper, we introduce CodEx, a modular framework for joint de-blurring and tomographic reconstruction that can effectively invert the motion blur introduced in sparse view fly-scanning. The method is a synergistic combination of a novel acquisition method with a novel non-convex Bayesian reconstruction algorithm. CodEx works by encoding the acquisition with a known binary code that the reconstruction algorithm then inverts. Using a well chosen binary code to encode the measurements can improve the accuracy of the inversion process. The CodEx reconstruction method uses the alternating direction method of multipliers (ADMM) to split the inverse problem into iterative deblurring and reconstruction sub-problems, making reconstruction practical to implement. We present reconstruction results on both simulated and binned experimental data to demonstrate the effectiveness of our method.
Year
DOI
Venue
2022
10.1109/TCI.2022.3197935
IEEE Transactions on Computational Imaging
Keywords
DocType
Volume
ADMM,coded exposure,computed tomography,deblurring,inverse problems,MBIR,motion-invariant imaging
Journal
8
ISSN
Citations 
PageRank 
2573-0436
0
0.34
References 
Authors
5
4
Name
Order
Citations
PageRank
Soumendu Majee100.34
Selin Aslan201.35
Doga Gursoy300.34
Charles A. Bouman42740473.62