论文标题
改进的迭代神经网络，用于双能量的高质量图像域材料分解
An Improved Iterative Neural Network for High-Quality Image-Domain Material Decomposition in Dual-Energy CT
论文作者
论文摘要
双能计算机断层扫描（DECT）已在需要材料分解的许多应用中广泛使用。图像域方法直接从高能衰减图像中直接分解材料图像，从而容易受到衰减图像上的噪声和伪影的影响。这项研究的目的是开发改进的迭代神经网络（INN），以在DECT中进行高质量的图像域材料分解，并研究其特性。我们提出了一种用于DECT材料分解的新酒店。拟议的创新体系结构在图像精炼模块中使用不同的跨物质卷积神经网络（CNN），并在图像重建模块中使用图像分解物理学。独特的跨物质CNN炼油厂结合了不同的编码过滤器和跨物质模型，可捕获不同材料之间的相关性。我们研究了具有基于斑块的重新制定和紧密框架条件的独特跨物质CNN炼油厂。 Numerical experiments with extended cardiactorso (XCAT) phantom and clinical data show that the proposed INN significantly improves the image quality over several image-domain material decomposition methods, including a conventional model-based image decomposition (MBID) method using an edge-preserving regularizer, a recent MBID method using pre-learned material-wise sparsifying transforms, and a noniterative deep CNN method.我们对基于贴片的重新制定的研究表明，学到的不同跨物质CNN炼油厂的过滤器可以大致满足紧密框架状况。
Dual-energy computed tomography (DECT) has been widely used in many applications that need material decomposition. Image-domain methods directly decompose material images from high- and low-energy attenuation images, and thus, are susceptible to noise and artifacts on attenuation images. The purpose of this study is to develop an improved iterative neural network (INN) for high-quality image-domain material decomposition in DECT, and to study its properties. We propose a new INN architecture for DECT material decomposition. The proposed INN architecture uses distinct cross-material convolutional neural network (CNN) in image refining modules, and uses image decomposition physics in image reconstruction modules. The distinct cross-material CNN refiners incorporate distinct encoding-decoding filters and cross-material model that captures correlations between different materials. We study the distinct cross-material CNN refiner with patch-based reformulation and tight-frame condition. Numerical experiments with extended cardiactorso (XCAT) phantom and clinical data show that the proposed INN significantly improves the image quality over several image-domain material decomposition methods, including a conventional model-based image decomposition (MBID) method using an edge-preserving regularizer, a recent MBID method using pre-learned material-wise sparsifying transforms, and a noniterative deep CNN method. Our study with patch-based reformulations reveals that learned filters of distinct cross-material CNN refiners can approximately satisfy the tight-frame condition.
