Book chapter · Conference paper
A New Iterative Method for CT Reconstruction with Uncertain View Angles
In this paper, we propose a new iterative algorithm for Computed Tomography (CT) reconstruction when the problem has uncertainty in the view angles. The algorithm models this uncertainty by an additive model-discrepancy term leading to an estimate of the uncertainty in the likelihood function. This means we can combine state-of-the-art regularization priors such as total variation with this likelihood.
To achieve a good reconstruction the algorithm alternates between updating the CT image and the uncertainty estimate in the likelihood. In simulated 2D numerical experiments, we show that our method is able to improve the relative reconstruction error and visual quality of the CT image for the uncertain-angle CT problem.
Language: | English |
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Publisher: | Springer |
Year: | 2019 |
Pages: | 156-167 |
Proceedings: | 7th International Conference on Scale Space and Variational Methods in Computer Vision |
Series: | Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Journal subtitle: | 7th International Conference, Ssvm 2019, Hofgeismar, Germany, June 30 – July 4, 2019, Proceedings |
ISBN: | 3030223671 , 303022368X , 303022368x , 9783030223670 and 9783030223687 |
ISSN: | 16113349 and 03029743 |
Types: | Book chapter and Conference paper |
DOI: | 10.1007/978-3-030-22368-7_13 |
ORCIDs: | 0000-0002-5243-0331 , Riis, Nicolai André Brogaard and Dong, Yiqiu |