Riabkov Dmitri Y, Di Bella Edward V R
Department of Physics, The University of Utah, 115 S, 1400 E, Salt Lake City, UT 84112, USA.
IEEE Trans Biomed Eng. 2002 Nov;49(11):1318-27. doi: 10.1109/TBME.2002.804588.
Compartment modeling of dynamic medical image data implies that the concentration of the tracer over time in a particular region of the organ of interest is well modeled as a convolution of the tissue response with the tracer concentration in the blood stream. The tissue response is different for different tissues while the blood input is assumed to be the same for different tissues. The kinetic parameters characterizing the tissue responses can be estimated by multichannel blind identification methods. These algorithms use the simultaneous measurements of concentration in separate regions of the organ; if the regions have different responses, the measurement of the blood input function may not be required. Three blind identification algorithms are analyzed here to assess their utility in medical imaging: eigenvector-based algorithm for multichannel blind deconvolution; cross relations; and iterative quadratic maximum-likelihood (IQML). Comparisons of accuracy with conventional (not blind) identification techniques where the blood input is known are made as well. Tissue responses corresponding to a physiological two-compartment model are primarily considered. The statistical accuracies of estimation for the three methods are evaluated and compared for multiple parameter sets. The results show that IQML gives more accurate estimates than the other two blind identification methods.
动态医学图像数据的房室建模意味着，在感兴趣器官的特定区域中，示踪剂随时间的浓度可以很好地建模为组织响应与血流中示踪剂浓度的卷积。不同组织的组织响应不同，而假定不同组织的血液输入相同。表征组织响应的动力学参数可以通过多通道盲识别方法进行估计。这些算法使用器官不同区域浓度的同步测量；如果这些区域具有不同的响应，则可能不需要测量血液输入函数。本文分析了三种盲识别算法，以评估它们在医学成像中的效用：基于特征向量的多通道盲反卷积算法；交叉关系；以及迭代二次最大似然法（IQML）。还将与已知血液输入的传统（非盲）识别技术进行准确性比较。主要考虑与生理双房室模型相对应的组织响应。针对多个参数集评估并比较了这三种方法估计的统计准确性。结果表明，IQML比其他两种盲识别方法给出的估计更准确。
