@inproceedings{5ff516b5fbe74dbca4938942fe7ca39c,
title = "Factorized Projection-Domain Spatio-Temporal Regularization for Dynamic Tomography",
abstract = "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.",
keywords = "Bilinear, Dynamic tomography, Partially-separable, Spatio-temporal regularization",
author = "Berk Iskender and Klasky, {Marc L.} and Patterson, {Brian M.} and Yoram Bresler",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
doi = "10.1109/ICASSP49357.2023.10095791",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",
address = "United States",
}