Uconnect: Synergistic Spectral CT Reconstruction With U-Nets Connecting the Energy Bins

Zhihan Wang, Alexandre Bousse, Franck Vermet, Jacques Froment, Béatrice Vedel, Alessandro Perelli, Jean-Pierre Tasu, Dimitris Visvikis

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Spectral computed tomography (CT) offers the possibility to reconstruct attenuation images at different energy levels, which can be then used for material decomposition. However, traditional methods reconstruct each energy bin individually and are vulnerable to noise. In this paper, we propose a novel synergistic method for spectral CT reconstruction, namely Uconnect. It utilizes trained convolutional neural networks (CNNs) to connect the energy bins to a latent image so that the full binned data is used synergistically. We experiment on two types of low-dose data: simulated and real patient data. Qualitative and quantitative analysis show that our proposed Uconnect outperforms state-of-art model-based iterative reconstruction (MBIR) techniques as well as CNN-based denoising.
Original languageEnglish
Pages (from-to)222-233
Number of pages12
JournalIEEE Transactions on Radiation and Plasma Medical Sciences
Issue number2
Publication statusE-pub ahead of print - 3 Nov 2023


  • Spectral Computed Tomography
  • Deep Learning
  • Synergistic Reconstruction
  • Regularization


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