A Dual-domain Refinement Network with FBP-based Jacobian Learning for Sparse-view Dual-Energy CT Material Decomposition
2026-06-29 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors study a way to improve dual-energy CT (DECT) imaging when fewer X-ray views are used, which normally makes it hard to separate materials accurately. They treat the problem mathematically as a nonlinear puzzle and create a special network called DECT-DRNet that repeatedly improves its guesses by combining known physics with deep learning. Their method uses a mix of image and frequency information to better reduce noise and keep details. The authors show that their approach leads to more precise material identification in tricky sparse-view DECT scans.
Dual-energy CT (DECT)Sparse-view imagingMaterial decompositionNonlinear least-squaresJacobian operatorFiltered back-projection (FBP)U-NetFourier convolutionDeep unrollingFrequency domain regularization
Authors
Qian Liu, Xiaohong Fan, Ke Chen, Chong Chen, Shuaikang Wang, Jianping Zhang
Abstract
Dual-energy CT (DECT) exploits attenuation differences across different X-ray spectra to provide richer material information and has been widely used in medical imaging. While sparse-view acquisition can lower radiation exposure, it makes DECT material decomposition even more challenging, as the problem is nonlinear and ill-posed. Existing deep unrolling approaches generally do not explicitly incorporate the Jacobian operator induced by the nonlinear forward model, and their sparsity priors are still mainly built on conventional convolutions, which are insufficient for modeling global structural information. This study addresses the challenge of DECT multi-material decomposition in sparse-view settings by representing it as a sparse-regularized nonlinear least-squares problem. To solve it, we propose an iterative dual-domain refinement network (DECT-DRNet). In each iteration, the filtered back-projection (FBP)-based Jacobian approximation module is used first to generate an intermediate material decomposition result. Here, we characterize the forward process of material decomposition using a nonlinear operator, and then construct a theoretically grounded learnable approximation of the adjoint Jacobian operator by integrating the FBP algorithm with a U-Net into the backward process. In addition, to address the limitation of existing deep learning-based decomposition methods in globally suppressing noise and artifacts, we introduce a learnable sparse dual domain regularization term that incorporates Fourier convolutional residual blocks. This refinement block combines geometric feature extraction in the image domain with noise suppression in the frequency domain, allowing the model to capture both global and local features while maintaining structural details. DECT-DRNet demonstrates its ability to achieve more accurate material decomposition under sparse-view conditions.