Low-dose cone-beam CT via raw counts domain low-signal correction schemes: Performance assessment and task-based parameter optimization (Part II. Task-based parameter optimization).
Low-dose cone-beam CT via raw counts domain low-signal correction schemes: Performance assessment and task-based parameter optimization (Part II. Task-based parameter optimization).
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DOI：
10.1002/mp.12855
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发表时间：
2018-05
期刊：
影响因子：
3.8
通讯作者：
Chen GH
中科院分区：
文献类型：
--
作者：
Gomez-Cardona D;Hayes JW;Zhang R;Li K;Cruz-Bastida JP;Chen GH
不同的低信号校正(LSC)方法已被证明可以有效地降低CT中的噪声条纹和噪声水平，以在低辐射剂量水平下提供可接受的图像。这些方法通常会导致CT图像具有高度的位移变化和各向异性的空间分辨率和噪声，这使得参数优化过程变得非常重要。这项工作的目的是为LSC方法开发一个基于局部任务的参数优化框架。以自适应修剪均值(ATM)滤波器和各向异性扩散(AD)滤波器为例，演示了如何使用基于任务的框架来优化滤波器参数的选择。在优化问题中，每个最小二乘法都包含两个参数，用集合表示。对于自动柜员机过滤器，这些参数是低和高信号阈值水平p1和ph；对于AD过滤器，参数是亮度梯度函数中的指数δ和γ。选取非预白化(NPW)数学观测器模型下的可检测性指数d‘作为参数优化的指标。该优化问题被描述为一个由最大化目标函数组成的无约束优化问题，其中i和j分别对应于第i个成像任务和第j个空间位置。由于对于每个LSC方法，没有显式的数学函数来描述d‘对参数集的依赖关系，所以通过在密集采样参数空间上的实验测量d’映射来解决优化问题。在这项工作中，定义了三个高对比度-高频辨别成像任务，以探索每种LSC方法的参数空间：垂直条形图案(任务I)、水平条形图案(任务II)和多方向特征(任务III)。分析考虑了两个空间位置，位于噪声条纹区域内的后方感兴趣区域(ROI)和距离噪声条纹区域更远的前方ROI。将基于任务的可检测性指数度量得到的最优结果与参数空间中具有不同噪声和空间分辨率权衡的其他工作点进行比较。通过d‘度量确定的最佳操作点取决于每个成像任务的主要空间频率分量与与LSC参数空间中的每个操作点相关联的高度移位变化和各向异性噪声和空间分辨率属性之间的相互作用。当特定成像任务的主要空间频率分量与空间分辨率损失方向或主要噪声空间频率分量重合时，这种交互作用对成像性能的影响最大；成像任务II的情况就是如此。成像任务I和III的性能受这种交互作用的影响比成像任务II小，因为任务I的主要频率分量垂直于成像任务II，而且成像任务III没有很强的方向依赖性。对于这两种LSC方法，轮廓的整体d‘的大小和形状与体模内的空间位置有很强的相关性，特别是对于成像任务II和III。当比较所研究的LSC方法时，在每个空间位置和成像任务的最佳操作点获得的d’值是相似的。提出了一种基于局部任务的可检测性框架，用于优化LSC方法的参数选择。该框架考虑了潜在的移位变化和各向异性的空间分辨率和噪声特性，以最大化CT系统的成像性能。给定LSC方法的最佳参数强烈依赖于图像对象内的空间位置。
Different low-signal correction (LSC) methods have been shown to efficiently reduce noise streaks and noise level in CT to provide acceptable images at low-radiation dose levels. These methods usually result in CT images with highly shift-variant and anisotropic spatial resolution and noise, which makes the parameter optimization process highly nontrivial. The purpose of this work was to develop a local task-based parameter optimization framework for LSC methods. Two well-known LSC methods, the adaptive trimmed mean (ATM) filter and the anisotropic diffusion (AD) filter, were used as examples to demonstrate how to use the task-based framework to optimize filter parameter selection. Two parameters, denoted by the set , for each LSC method were included in the optimization problem. For the ATM filter, these parameters are the low- and high-signal threshold levels pl and ph; for the AD filter, the parameters are the exponents δ and γ in the brightness gradient function. The detectability index d′ under the non-prewhitening (NPW) mathematical observer model was selected as the metric for parameter optimization. The optimization problem was formulated as an unconstrained optimization problem that consisted of maximizing an objective function , where i and j correspond to the i-th imaging task and j-th spatial location, respectively. Since there is no explicit mathematical function to describe the dependence of d′ on the set of parameters for each LSC method, the optimization problem was solved via an experimentally measured d′ map over a densely sampled parameter space. In this work, three high-contrast–high-frequency discrimination imaging tasks were defined to explore the parameter space of each of the LSC methods: a vertical bar pattern (task I), a horizontal bar pattern (task II), and a multidirectional feature (task III). Two spatial locations were considered for the analysis, a posterior region-of-interest (ROI) located within the noise streaks region and an anterior ROI, located further from the noise streaks region. Optimal results derived from the task-based detectability index metric were compared to other operating points in the parameter space with different noise and spatial resolution trade-offs. The optimal operating points determined through the d′ metric depended on the interplay between the major spatial frequency components of each imaging task and the highly shift-variant and anisotropic noise and spatial resolution properties associated with each operating point in the LSC parameter space. This interplay influenced imaging performance the most when the major spatial frequency component of a given imaging task coincided with the direction of spatial resolution loss or with the dominant noise spatial frequency component; this was the case of imaging task II. The performance of imaging tasks I and III was influenced by this interplay in a smaller scale than imaging task II, since the major frequency component of task I was perpendicular to imaging task II, and because imaging task III did not have strong directional dependence. For both LSC methods, there was a strong dependence of the overall d′ magnitude and shape of the contours on the spatial location within the phantom, particularly for imaging tasks II and III. The d′ value obtained at the optimal operating point for each spatial location and imaging task was similar when comparing the LSC methods studied in this work. A local task-based detectability framework to optimize the selection of parameters for LSC methods was developed. The framework takes into account the potential shift-variant and anisotropic spatial resolution and noise properties to maximize the imaging performance of the CT system. Optimal parameters for a given LSC method depend strongly on the spatial location within the image object.
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影响因子：
3.8
作者：
Leng, Shuai;Yu, Lifeng;McCollough, Cynthia H.
通讯作者：
McCollough, Cynthia H.
影响因子：
3.5
作者：
Gang GJ;Siewerdsen JH;Webster Stayman J
通讯作者：
Webster Stayman J
DOI：
10.1073/pnas.90.21.9758
发表时间：
1993-11-01
影响因子：
11.1
作者：
BARRETT, HH;YAO, J;MYERS, KJ
通讯作者：
MYERS, KJ
影响因子：
19.7
作者：
Hamberg, LM;Rhea, JT;Thrall, JH
通讯作者：
Thrall, JH
影响因子：
3.8
作者：
Hsieh, Scott S.;Pelc, Norbert J.
通讯作者：
Pelc, Norbert J.