Multi-material decomposition (MMD) technique decomposes the CT images into basis material images and has been promising in clinical practice for material composition quantification within the human body. MMD could be implemented using the image data acquired from spectral CT or its special case, dual-energy CT (DECT) while the spectral CT data acquisition usually requires a hardware modification. In this paper, we propose an image domain MMD method using single energy CT (SECT). The proposed objective function applies a least square data fidelity term to enforce the minimization between the linear combination of decomposed material image and the measured SECT image, and an edge-preserving (EP) regularization term to meet the piecewise constant property of the material image. We apply the optimization transfer principle to form a pixel-wise separable quadratic surrogate (PWSQS) function in each iteration to decrease the objective function. The pixelwise direct inversion method assisted by the two-material assumption (TMA) is applied to obtain a good initial value. The proposed method is evaluated using a digital phantom, a Catphan phantom and the clinical data. A low-pass filtration method is implemented for a comparison purpose. In the phantom study, the proposed TMA method achieves high volume fraction accuracy (VFA) of 79.64% and the proposed EP method further increases the VFA by 15.56% and decreases the decomposition standard deviation (STD) by 81.51% compared with the TMA method. At the comparable noise level, the proposed EP method increases spatial resolution by an overall factor of 1.01 when the modulation transfer function magnitude is decreased to 50% compared with the low-pass filtration method. In clinical data study, the virtual non-contrast image generated by the proposed method achieves the root-mean-squared-relative error of 2.93% compared with the contrast-free ground-truth image.

中文翻译：

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使用单能CT进行图像域多材料分解。

多材料分解（MMD）技术将CT图像分解为基本的材料图像，并且在临床实践中有望用于人体中的材料成分定量。MMD可以使用从光谱CT或其特殊情况下的双能CT（DECT）采集的图像数据来实现，而光谱CT数据采集通常需要进行硬件修改。在本文中，我们提出了一种使用单能CT（SECT）的图像域MMD方法。提出的目标函数应用最小二乘数据保真度项来强制分解的实物图像和测量的SECT图像的线性组合之间的最小化，以及保留边缘（EP）的正则化项以满足材料图像的分段恒定特性。我们应用优化转移原理在每次迭代中形成一个像素级可分离的二次代理（PWSQS）函数，以减少目标函数。应用由两种材料假设（TMA）辅助的逐像素直接反演方法以获得良好的初始值。使用数字体模，猫体模和临床数据对提出的方法进行评估。为了比较目的，实施了低通过滤方法。在幻像研究中，与TMA方法相比，拟议的TMA方法可实现79.64％的高体积分数准确度（VFA），而EP方法可进一步提高VFA 15.56％并降低分解标准偏差（STD）81.51％。在可比较的噪声水平下，建议的EP方法将空间分辨率提高了1倍。与低通滤波方法相比，当调制传递函数幅度减小到50％时为01。在临床数据研究中，与无对比度地面图像相比，该方法生成的虚拟非对比度图像的均方根相对误差为2.93％。