IEEE Trans Med Imaging. 2020 Apr;39(4):1223-1234. doi: 10.1109/TMI.2019.2946177. Epub 2019 Oct 8.
Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the decomposed material images can be severely degraded by noise and artifacts. This paper proposes a new method dubbed DECT-MULTRA for image-domain DECT material decomposition that combines conventional penalized weighted-least squares (PWLS) estimation with regularization based on a mixed union of learned transforms (MULTRA) model. Our proposed approach pre-learns a union of common-material sparsifying transforms from patches extracted from all the basis materials, and a union of cross-material sparsifying transforms from multi-material patches. The common-material transforms capture the common properties among different material images, while the cross-material transforms capture the cross-dependencies. The proposed PWLS formulation is optimized efficiently by alternating between an image update step and a sparse coding and clustering step, with both of these steps having closed-form solutions. The effectiveness of our method is validated with both XCAT phantom and clinical head data. The results demonstrate that our proposed method provides superior material image quality and decomposition accuracy compared to other competing methods.
双能 CT（DECT）成像由于其物质分解能力，在高级成像应用中发挥着重要作用。基于图像域的分解直接在 CT 图像上进行线性矩阵反演，但分解后的物质图像会受到噪声和伪影的严重影响。本文提出了一种新的基于图像域 DECT 物质分解的方法，称为 DECT-MULTRA，它将传统的基于惩罚的加权最小二乘（PWLS）估计与基于混合学习变换联合（MULTRA）模型的正则化相结合。我们的方法从所有基础物质的斑块中预学习共同物质稀疏变换的联合，以及从多物质斑块中学习交叉物质稀疏变换的联合。常见物质变换捕获不同物质图像之间的共同属性，而交叉物质变换则捕获交叉依赖性。我们的 PWLS 公式通过在图像更新步骤和稀疏编码与聚类步骤之间交替优化，这两个步骤都有闭式解。我们的方法使用 XCAT 体模和临床头部数据进行了有效性验证。结果表明，与其他竞争方法相比，我们提出的方法提供了更好的物质图像质量和分解准确性。
