An unbiased parametric imaging algorithm for nonuniformly sampled biomedical system parameter estimation
An unbiased parametric imaging algorithm for nonuniformly sampled biomedical system parameter estimation
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DOI：
10.1109/42.511754
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发表时间：
1996-08-01
影响因子：
10.6
通讯作者：
Wang, ZZ
中科院分区：
文献类型：
--
作者：
Feng, DG;Huang, SC;Wang, ZZ
提出了一种用于非均匀采样生物医学系统参数估计的无偏广义线性最小二乘（GLLS）算法。该算法消除了非线性最小回归所需的初始值和计算负担，在估计偏差和标准差方面达到了相当的估计质量，因此，该算法在图像范围（逐像素）参数估计中特别有用，例如，用正电子发射断层扫描从示踪剂动态研究中生成参数图像。最后通过实例验证了该方法的有效性。该算法也普遍适用于其他连续系统参数估计。
An unbiased algorithm of generalized linear least squares (GLLS) for parameter estimation of nonuniformly sampled biomedical systems is proposed. The basic theory and detailed derivation of the algorithm are given, This algorithm removes the initial values required and computational burden of nonlinear least regression and achieves a comparable estimation quality in terms of the estimates' bias and standard deviation, Therefore, this algorithm is particular useful in image-wide (pixel-by-pixel based) parameter estimation, e.g., to generate parametric images from tracer dynamic studies with positron emission tomography. An example is presented to demonstrate the performance of this new technique. This algorithm is also generally applicable to other continuous system parameter estimation.