kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Photon-Counting CT Reconstruction With a Learned Forward Operator
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).ORCID iD: 0000-0001-7372-8535
KTH, School of Engineering Sciences (SCI), Physics, Physics of Medical Imaging. Karolinska Univ Hosp, BioClinicum, MedTech Labs, SE-17164 Solna, Sweden..ORCID iD: 0000-0002-5092-8822
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.). KTH, School of Engineering Sciences (SCI), Physics.ORCID iD: 0000-0001-8969-4253
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.). Uppsala Univ, Dept Informat Technol, S-75105 Uppsala, Sweden..ORCID iD: 0000-0002-1118-6483
2022 (English)In: IEEE Transactions on Computational Imaging, ISSN 2573-0436, E-ISSN 2333-9403, Vol. 8, p. 536-550Article in journal (Refereed) Published
Abstract [en]

Photon-Counting CT is an emerging imaging technology that promises higher spatial resolution and the possibility for material decomposition in the reconstruction. A major difficulty in Photon-Counting CT is to efficiently model cross-talk between detectors. In this work, we accelerate image reconstruction tasks for Photon-Counting CT by modelling the cross-talk with an appropriately trained deep convolutional neural network. The main result relates to proving convergence when using such a learned cross-talk model in the context of second-order optimisation methods for spectral CT. Another is to evaluate the method through numerical experiments on small-scale CT acquisitions generated using a realistic physics model. Using the reconstruction with a full cross-talk model as ground truth, the learned cross-talk model results in a 20 dB increase in peak-signal-to noise ratio compared to ignoring crass-talk altogether. At the same time, it effectively cuts the computation time of the full cross-talk model in half. Furthermore, the learned cross-talk model generalises well to both unseen data and unseen detector settings. Our results indicate that such a partially learned forward operator is a suitable way of modelling data generation in Photon-Counting CT with a computational benefit that becomes more noticeable for realistic problem sizes.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. Vol. 8, p. 536-550
Keywords [en]
Photon-counting, computed tomography, spectral CT, regularisation, deep learning, detector cross-talk
National Category
Other Computer and Information Science Information Systems Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-316708DOI: 10.1109/TCI.2022.3183405ISI: 000838365600001Scopus ID: 2-s2.0-85132610473OAI: oai:DiVA.org:kth-316708DiVA, id: diva2:1693166
Note

QC 20220906

Available from: 2022-09-06 Created: 2022-09-06 Last updated: 2022-09-06Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Ström, EmanuelPersson, MatsEguizabal, AlmaÖktem, Ozan

Search in DiVA

By author/editor
Ström, EmanuelPersson, MatsEguizabal, AlmaÖktem, Ozan
By organisation
Mathematics (Dept.)Physics of Medical ImagingMathematics (Div.)Physics
In the same journal
IEEE Transactions on Computational Imaging
Other Computer and Information ScienceInformation SystemsComputer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 119 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf