Image Domain Dual Material Decomposition for Dual-Energy CT using Butterfly Network

Abstract

Dual-energy CT (DECT) has been increasingly used in imaging applications because of its capability for material differentiation. However, material decomposition suffers from magnified noise from two CT images of independent scans, leading to severe degradation of image quality. Existing algorithms achieve suboptimal decomposition performance since they fail to accurately depict the mapping relationship between DECT and the basis material images. Inspired by the impressive potential of CNN, we developed a new Butterfly network to perform the image domain dual material decomposition due to its strong approximation ability to the mapping functions in DECT. The Butterfly network is derived from the image domain DECT decomposition model by exploring the geometric relationship between mapping functions of data model and network components. The network is designed as the double-entry double-out crossover architecture based on the decomposition formulation. It enters a pair of dual-energy images as inputs and defines the ground true decomposed images as each label. The crossover architecture, which plays an important role in material decomposition, is designed to implement the information exchange between the two material generation pathways in the network. Network components exhibit different sensitivity to basis materials in the visualization. By analyzing their sensitivity to different basis materials, we determined the roles of network components in material decomposition. This visualization evaluation reveals what the network can learn and verifies the rationality of network design. The qualitative and quantitative evaluations in material decomposition of patient data indicate that the proposed network outperforms its counterpart.

References in corpus (4)

• Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
• A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction
• An algorithm for constrained one-step inversion of spectral CT data
• Joint Reconstruction of Multi-channel, Spectral CT Data via Constrained Total Nuclear Variation Minimization

Cited by in corpus (6)

• A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks
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• Photon-Counting CT in Cancer Radiotherapy: Technological Advances and Clinical Benefits
• Characterization of breast tissues in density and effective atomic number basis via spectral X-ray computed tomography
• Material decomposition from a single x-ray projection via single-grid phase contrast imaging
• Fast Iterative Reconstruction for Multi-spectral CT by a Schmidt Orthogonal Modification Algorithm (SOMA)