Fast formation of statistically reliable FDG parametric images based on clustering and principal components
Fast formation of statistically reliable FDG parametric images based on clustering and principal components
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DOI：
10.1088/0031-9155/47/3/307
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发表时间：
2002-02-07
影响因子：
3.5
通讯作者：
Alpert, NM
中科院分区：
文献类型：
--
作者：
Kimura, Y;Senda, M;Alpert, NM
参数图像的形成需要逐体素估计命运常数，这是一个对噪声敏感且计算量大的过程。将基于模型的二参数模型聚类方法（CAKS）推广到FDG三参数模型。其概念是将具有相似动力学特征的体素平均化以减少噪声。通过所有体素的组织时间-活性曲线的前两个主成分对体素动力学进行分类。k（2）和k（3）是逐簇估计的，K-1是簇内逐体素估计的。当CAKS被应用于具有类似于脑FDG扫描的噪声水平的模拟图像时，估计偏差被很好地抑制，并且估计误差基本上小于传统的基于体素的估计的误差，对于Ki为1.3倍，对于k（3）为1.5倍。CAKS的体素水平估计的统计可靠性与包括100个体素的ROI分析相当。将CAKS应用于阿尔茨海默病（ALZ）和皮质基底变性（CBD）的临床病例。在ALZ中，受影响的区域具有低Ki（K（1）k（3）/（k（2）+ k（3）和k（3）。在CBD中，K-i较低，但k（3）保持不变。这些结果与基于ROI的动力学分析一致。由于CAKS减少了调用估计的数量，计算时间大大减少。总之，CAKS已被扩展到允许三房室模型的参数成像。该方法计算效率高，具有低偏差和优良的噪声特性。
Formation of parametric images requires voxel-by-voxel estimation of fate constants, a process sensitive to noise and computationally demanding. A model-based clustering method for a two-parameter model (CAKS) was extended to the FDG three-parameter model. The concept was to average voxels with similar kinetic signatures to reduce noise. Voxel kinetics were categorized by the first two principal components of the tissue time-activity curves for all voxels. k(2) and k(3) were estimated cluster-by-cluster, and K-1 was estimated voxel-by-voxel within clusters. When CAKS was applied to simulated images with noise levels similar to brain FDG scans, estimation bias was well suppressed, and estimation errors were substantially smaller-1.3 times for K-i and 1.5 times for k(3)-than those of conventional voxel-based estimation. The statistical reliability of voxel-level estimation by CAKS was comparable with ROI analysis including 100 voxels. CAKS was applied to clinical cases with Alzheimer's disease (ALZ) and cortico basal degeneration (CBD). In ALZ, the affected regions had low K-i(K(1)k(3)/(k(2) + k(3))) and k(3). In CBD, K-i was low, but k(3) was preserved. These results were consistent with ROI-based kinetic analysis. Because CAKS decreased (lie number of invoked estimations, the calculation time was reduced substantially. In conclusion, CAKS has been extended to allow parametric imaging of a three-compartment model. The method is computationally efficient, with low bias and excellent noise properties.