Factorized Projection-Domain Spatio-Temporal Regularization for Dynamic Tomography

Dynamic tomography is an ill-posed inverse problem where the object evolves during the sequential acquisition of projections. The goal is to reconstruct the object for each time instant. However, performing a direct reconstruction using this inconsistent set of projections is impossible. In this pap...

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Bibliographic Details
Main Authors: Iskender, Berk, Klasky, Marc L., Patterson, Brian M., Bresler, Yoram
Format: Conference Proceeding
Language:English
Subjects:
Acoustics
Bilinear
Computed tomography
Dynamic tomography
Heuristic algorithms
Inverse problems
Partially-separable
Signal processing
Signal processing algorithms
Spatio-temporal regularization
Speech processing
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Summary:Dynamic tomography is an ill-posed inverse problem where the object evolves during the sequential acquisition of projections. The goal is to reconstruct the object for each time instant. However, performing a direct reconstruction using this inconsistent set of projections is impossible. In this paper, we propose an object-domain recovery algorithm using a variational formulation that combines a partially separable spatio-temporal prior with a basic total-variation spatial regularization for improved performance, while preserving full interpretability. Numerical experiments on data derived from real object CT data demonstrate the advantages of the proposed algorithm over recent projection-domain and deep-prior-based methods.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10095791