Wang Wenying, Tilley Steven, Tivnan Matthew, Stayman J Webster
Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, USA 21205.
Proc SPIE Int Soc Opt Eng. 2019 Jun;11072. doi: 10.1117/12.2534437. Epub 2019 May 28.
Spectral CT is an emerging modality that permits material decomposition and density estimation through the use of energy-dependent information in measurements. Direct model-based material decomposition algorithms have been developed that incorporate statistical models and advanced regularization schemes to improve density estimates and lower exposure requirements. However, understanding and control of the relationship between regularization and image properties is complex with interactions between spectral channels and material bases. In particular, regularization in one material basis can affect the image properties of other material bases, and vice versa. In this work, we derived a closed-form set of local impulse responses for the solutions to a general, regularized, model-based material decomposition (MBMD) objective. These predictors quantify both the spatial resolution in each material image as well as the influence of regularization of one material basis on other material images. This information can be used prospectively to tune regularization parameters for specific imaging goals.
光谱CT是一种新兴的成像方式，它通过利用测量中与能量相关的信息来实现物质分解和密度估计。已经开发出基于直接模型的物质分解算法，这些算法纳入了统计模型和先进的正则化方案，以改善密度估计并降低辐射剂量要求。然而，由于光谱通道和物质基之间的相互作用，对正则化与图像特性之间关系的理解和控制较为复杂。特别是，一种物质基中的正则化会影响其他物质基的图像特性，反之亦然。在这项工作中，我们为一般的、正则化的、基于模型的物质分解（MBMD）目标的解导出了一组封闭形式的局部脉冲响应。这些预测器既量化了每个物质图像中的空间分辨率，也量化了一种物质基的正则化对其他物质图像的影响。这些信息可用于前瞻性地调整正则化参数以实现特定的成像目标。
