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

TL;DR

This paper introduces a Butterfly network for dual-energy CT material decomposition, leveraging its strong approximation capabilities to improve image quality and material differentiation in DECT imaging.

Contribution

The paper proposes a novel Butterfly network architecture tailored for image domain dual material decomposition in DECT, inspired by the geometric relationship of the data model.

Findings

Outperforms existing algorithms in decomposition accuracy.

Effectively reduces noise amplification in dual-energy CT images.

Provides insights into network component roles through sensitivity analysis.

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.