Mirzaei Fazel, Faghihi Reza
Medical Radiation Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran.
BJR Open. 2019 Apr 30;1(1):20180008. doi: 10.1259/bjro.20180008. eCollection 2019.
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.
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.
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.
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.
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.
双能CT（DECT）是一种成像方式，通过两种不同的能谱对物体进行扫描。利用这两种测量结果，可以分离出两种类型的物质，并生成密度图像对。在临床和工业CT应用中，分解两种以上的物质是必要的。
在我们的多物质分解（MMD）中，使用一种创新的局部聚类方法选择重心坐标。局部聚类通过减小搜索域提高了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，使用了快速双向豪斯多夫距离测量。为了应对噪声这一重大障碍，我们使用了双局部维纳滤波方向窗口（DLWFDW）算法。
简而言之，所提出的算法在临床图像上分别以小于2%和9%的误差分离出血液和脂肪感兴趣区域（ROI）。此外，还利用体模数据评估了分解不同浓度不同物质的能力。在分离不同浓度不同物质时获得的最高准确率分别为93%（对于钙斑）和97.1%（对于碘造影剂）。在以下结果部分将详细讨论所获得的结果。
在本研究中，我们提出了一种新的物质分解算法。它通过采用易于实现的工具改进了MMD工作流程。此外，在本研究中，通过在物质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，所提出方法的性能与现有方法相当。
所有分解方法都有其特定问题。图像域分解也有障碍和问题，包括需要一个预定表格来分离具有指定坐标的不同物质。在本研究中，试图通过使用聚类方法并依赖衰减域中不同物质之间的间隔来解决这个问题。
