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Deep Learning-Based Dictionary Learning and Tomographic Image Reconstruction
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).ORCID iD: 0000-0002-6648-2378
Philips Res, Hamburg, Germany..
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).ORCID iD: 0000-0002-1118-6483
2022 (English)In: SIAM Journal on Imaging Sciences, E-ISSN 1936-4954, Vol. 15, no 4, p. 1729-1764Article in journal (Refereed) Published
Abstract [en]

This work presents an approach for image reconstruction in clinical low-dose tomography that combines principles from sparse signal processing with ideas from deep learning. First, we describe sparse signal representation in terms of dictionaries from a statistical perspective and interpret dictionary learning as a process of aligning the distribution that arises from a generative model with the empirical distribution of true signals. As a result, we can see that sparse coding with learned dictionaries resembles a specific variational autoencoder, where the encoder is a sparse coding algorithm and the decoder is a linear function. Next, we show that dictionary learning can also benefit from computational advancements introduced in the context of deep learning, such as parallelism and stochastic optimization. Finally, we show that regularization by dictionaries achieves competitive performance in computed tomography reconstruction compared to state-of-the-art model-based and data-driven approaches, while being unsupervised with respect to tomographic data.

Place, publisher, year, edition, pages
Society for Industrial & Applied Mathematics (SIAM) , 2022. Vol. 15, no 4, p. 1729-1764
Keywords [en]
dictionary learning, generative model, deep learning, image reconstruction, computed tomography
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-323412DOI: 10.1137/21M1445697ISI: 000903981200005OAI: oai:DiVA.org:kth-323412DiVA, id: diva2:1732967
Note

QC 20230201

Available from: 2023-02-01 Created: 2023-02-01 Last updated: 2024-04-25Bibliographically approved

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Rudzusika, JevgenijaÖktem, Ozan

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CiteExportLink to record
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  • de-DE
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  • en-US
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