Maitra R
Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, USA.
Med Image Anal. 1998 Dec;2(4):379-93. doi: 10.1016/s1361-8415(98)80018-2.
Parametric imaging procedures offer the possibility of comprehensive assessment of tissue metabolic activity. Estimating variances of these images is important for the development of inference tools in a diagnostic setting. However, these are not readily obtained because the complexity of the radio-tracer models used in the generation of a parametric image makes analytic variance expressions intractable. On the other hand, a natural extension of the usual bootstrap resampling approach is infeasible because of the expanded computational effort. This paper suggests a computationally practical, approximate simulation strategy to variance estimation. Results of experiments done to evaluate the approach in a simplified model one-dimensional problem are very encouraging. Diagnostic checks performed on a single real-life positron emission tomography (PET) image to test for the feasibility of applying the procedure in a real-world PET setting also show some promise. The suggested methodology is evaluated here in the context of parametric images extracted by mixture analysis; however, the approach is general enough to extend to other parametric imaging methods.
参数成像程序提供了全面评估组织代谢活性的可能性。估计这些图像的方差对于在诊断环境中开发推理工具很重要。然而，这些方差不容易获得，因为生成参数图像时使用的放射性示踪剂模型的复杂性使得解析方差表达式难以处理。另一方面，由于计算量的增加，通常的自助重采样方法的自然扩展是不可行的。本文提出了一种计算上可行的、近似的方差估计模拟策略。在简化模型的一维问题中评估该方法的实验结果非常令人鼓舞。在单个实际正电子发射断层扫描（PET）图像上进行的诊断检查，以测试该程序在实际PET环境中应用的可行性，也显示出一些前景。这里在通过混合分析提取的参数图像的背景下评估了所建议的方法；然而，该方法足够通用，可以扩展到其他参数成像方法。
