客观双能 CT (DECT) 是一种通过两种不同能谱扫描物体的成像方式。使用这两种测量，可以分离两种类型的材料，也可以生成密度图像对。在临床和工业 CT 应用中都需要分解两种以上的材料。方法 在我们的 MMD 中，使用创新的局部聚类方法选择重心坐标。局部聚类通过减少搜索域来提高重心坐标分配的精度。因此该算法可以并行运行。为了优化坐标选择，使用快速双向豪斯多夫距离测量。为了解决噪声的重大障碍，我们使用了双局部维纳滤波器方向窗（DLWFDW）算法。结果 简而言之，所提出的算法在临床图像上分离血液和脂肪 ROI，误差分别小于 2% 和 9%。此外，还利用模型数据评估分解不同浓度的不同材料的能力。分离不同浓度的不同物质时获得的最高准确度分别为 93%（对于钙斑）和 97.1%（对于碘造影剂）。所获得的结果将在下面的结果部分详细讨论。结论在这项研究中，我们提出了一种新的材料分解算法。它通过使用易于实现的工具改进了MMD工作流程。此外，在本研究中，通过在材料坐标分配中采用聚类概念，努力将MMD算法转变为半自动算法。从定性和定量方面来看，该方法的性能与现有方法相当。 知识进展 所有分解方法都有其自身的具体问题。图像域分解也存在障碍和问题，包括需要一个预定的表格来分离具有指定坐标的不同材料。在本研究中，试图通过使用聚类方法并依靠衰减域中不同材料之间的间隔来解决这个问题。

Quantification of contrast agent materials using a new image- domain multi material decomposition algorithm based on dual energy CT.

OBJECTIVE Dual-Energy CT (DECT) is an imaging modality in which the objects are scanned by two different energy spectra. Using these two measurements, two type of materials can be separated and density image pairs can be generated as well. Decomposing more than two materials is necessary in both clinical and industrial CT applications. METHODS In our MMD, barycentric coordinates were chosen using an innovative local clustering method. Local clustering increases precision in the barycentric coordinates assignment by decreasing search domain. Therefore the algorithm can be run in parallel. For optimizing coordinates selection, a fast bi-directional Hausdorff distance measurement is used. To deal with the significant obstacle of noise, we used Doubly Local Wiener Filter Directional Window (DLWFDW) algorithm. RESULTS Briefly, the proposed algorithm separates blood and fat ROIs with errors of less than 2 and 9 % respectively on the clinical images. Also, the ability to decompose different materials with different concentrations is evaluated employing the phantom data. The highest accuracy obtained in separating different materials with different concentrations was 93 % (for calcium plaque) and 97.1 % (for iodine contrast agent) respectively. The obtained results discussed in detail in the following results section. CONCLUSION In this study, we propose a new material decomposition algorithm. It improves the MMD work flow by employing tools which are easy to implement. Furthermore, in this study, an effort has been made to turn the MMD algorithm into a semi-automatic algorithm by employing clustering concept in material coordinate's assignment. The performance of the proposed method is comparable to existing methods from qualitative and quantitative aspects. ADVANCES IN KNOWLEDGE All decomposition methods have their own specific problems. Image- domain decomposition also has barriers and problems, including the need for a predetermined table for the separation of different materials with specified coordinates. In the present study, it attempts to solve this problem by using clustering methods and relying on the intervals between different materials in the attenuation domain.