Quantitative material characterization from multi-energy photon counting CT
Quantitative material characterization from multi-energy photon counting CT
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DOI：
10.1118/1.4790692
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发表时间：
2013-03-01
期刊：
影响因子：
3.8
通讯作者：
MacDonald, Lawrence R.
中科院分区：
文献类型：
--
作者：
Alessio, Adam M.;MacDonald, Lawrence R.
目的：为了量化水，脂肪和钙的软组织成分的浓度，通过分解的X-射线光谱签名在multi-energy CT images.Methods：分解的双能和多能X-射线数据到基础材料可以在投影域，图像域，或在图像重建。在这项工作中，作者提出了在图像域中的软组织定征的应用程序的多能量X射线数据的分解方法。为了证明原理，作者将之前提出的几种方法和一种新型内容感知方法应用于使用原型光子计数CT系统获取的多能量图像。从幻影和离体标本的数据evaluated.Results：在一个区域中的材料的数量和类型可以限制基于先验知识或分类策略。所提出的差异分类器成功地将图像分为空气，水+脂肪，水+脂肪+碘和水+钙区域。然后，基于加权最小二乘优化的内容感知材料分解生成定量的浓度图。体模研究中水和油成分浓度估计的偏倚为
Purpose: To quantify the concentration of soft-tissue components of water, fat, and calcium through the decomposition of the x-ray spectral signatures in multi-energy CT images.Methods: Decomposition of dual-energy and multi-energy x-ray data into basis materials can be performed in the projection domain, image domain, or during image reconstruction. In this work, the authors present methodology for the decomposition of multi-energy x-ray data in the image domain for the application of soft-tissue characterization. To demonstrate proof-of-principle, the authors apply several previously proposed methods and a novel content-aware method to multi-energy images acquired with a prototype photon counting CT system. Data from phantom and ex vivo specimens are evaluated.Results: The number and type of materials in a region can be limited based on a priori knowledge or classification strategies. The proposed difference classifier successfully classified the image into air only, water+fat, water+fat+iodine, and water+calcium regions. Then, the content-aware material decomposition based on weighted least-square optimization generated quantitative maps of concentration. Bias in the estimation of the concentration of water and oil components in a phantom study was