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Article Dans Une Revue IEEE Transactions on Radiation and Plasma Medical Sciences Année : 2024

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

Résumé

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 article, 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: 1) simulated and 2) real patient data. Qualitative and quantitative analysis show that our proposed Uconnect outperforms state-of-the-art model-based iterative reconstruction (MBIR) techniques as well as CNN-based denoising.

Dates et versions

hal-04441618 , version 1 (06-02-2024)

Identifiants

Citer

Zhihan Wang, Alexandre Bousse, Franck Vermet, Jacques Froment, Béatrice Vedel, et al.. Uconnect: Synergistic Spectral CT Reconstruction With U-Nets Connecting the Energy Bins. IEEE Transactions on Radiation and Plasma Medical Sciences, 2024, 8 (2), pp.222-233. ⟨10.1109/TRPMS.2023.3330045⟩. ⟨hal-04441618⟩
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