Uconnect: Synergistic Spectral CT Reconstruction with U-Nets Connecting the Energy bins
arXiv:2311.00666 · doi:10.1109/TRPMS.2023.3330045
Abstract
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.
References in corpus (13)
- Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
- Tensor-based formulation and nuclear norm regularization for multi-energy computed tomography
- Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm
- Joint Reconstruction of Multi-channel, Spectral CT Data via Constrained Total Nuclear Variation Minimization
- Coupled Dictionary Learning for Multi-contrast MRI Reconstruction
- Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
- Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey
- Systematic Review on Learning-based Spectral CT
- Synergistic Multi-spectral CT Reconstruction with Directional Total Variation
- Sparse-View Spectral CT Reconstruction Using Deep Learning
- Regularising Inverse Problems with Generative Machine Learning Models
- Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction
- Synergistic PET/CT Reconstruction Using a Joint Generative Model