PDF(Pubmed)
Abstract：
Photon-counting CT systems generally allow for acquiring multiple spectral datasets and thus for decomposing CT images into multiple materials. We introduce a prior knowledge-free deterministic material decomposition approach for quantifying three material concentrations on a commercial photon-counting CT system based on a single CT scan. We acquired two phantom measurement series: one to calibrate and one to test the algorithm. For evaluation, we used an anthropomorphic abdominal phantom with inserts of either aqueous iodine solution, aqueous tungsten solution, or water. Material CT numbers were predicted based on a polynomial in the following parameters: Water-equivalent object diameter, object center-to-isocenter distance, voxel-to-isocenter distance, voxel-to-object center distance, and X-ray tube current. The material decomposition was performed as a generalized least-squares estimation. The algorithm provided material maps of iodine, tungsten, and water with average estimation errors of 4% in the contrast agent maps and 1% in the water map with respect to the material concentrations in the inserts. The contrast-to-noise ratio in the iodine and tungsten map was 36% and 16% compared to the noise-minimal threshold image. We were able to decompose four spectral images into iodine, tungsten, and water.
摘要：
光子计数CT系统通常允许采集多个光谱数据集并且因此允许将CT图像分解成多种材料。我们引入了一种先验的无知识的确定性材料分解方法，用于在基于单个CT扫描的商用光子计数CT系统上量化三种材料浓度。我们获得了两个体模测量系列：一个用于校准，一个用于测试算法。为了评估，我们使用了一个拟人化的腹部模型，里面有两种碘水溶液，钨水溶液，或者水。材料CT数是根据以下参数的多项式预测的：水当量物体直径，对象中心到等中心的距离，体素到等中心的距离，体素到对象中心的距离，和X射线管电流。材料分解作为广义最小二乘估计进行。该算法提供了碘的物质图，钨,和水，相对于插入物中的材料浓度，造影剂图的平均估计误差为4％，水图的平均估计误差为1％。与噪声最小阈值图像相比，碘和钨图的对比度噪声比为36％和16％。我们能够将四张光谱图像分解成碘，钨,和水。
