Sutton D G, Kempi V
Department of Medical Physics, Ninewells Hospital and Medical School, Dundee, UK.
Phys Med Biol. 1992 Jan;37(1):53-67. doi: 10.1088/0031-9155/37/1/004.
Before deconvolution can be used in renography, it is necessary to decide whether the renal function is sufficiently good to allow it. To see if this decision can be circumvented, an iterative constrained least-squares restoration (CLSR) method was implemented in which the point of termination of the iteration occurs when a residual vector has a value less than an estimate of the noise in the original renogram curve. The technique was compared with the matrix algorithm and with direct FFT division. The comparison was achieved by deconvolving simulated renogram data with differing transit time spectra and statistics. As expected, the FFT technique produced results of little value whereas the CLSR and matrix methods produced values of mean transit time (MTT) that differed slightly from the expected results. Analysis indicated that the matrix approach was superior when the percentage noise component was less than 6% and vice versa. No technique produced useful transit time spectra. As the CLSR technique produced better results than the matrix method in simulations with relatively long MTTs and high noise, it seems reasonable to suggest that it might be used for renogram deconvolution without the need for previous inspection of the curves.
在反卷积可用于肾图分析之前，有必要确定肾功能是否足够良好以允许使用它。为了查看是否可以规避这一判断，实施了一种迭代约束最小二乘恢复（CLSR）方法，其中当残差向量的值小于原始肾图曲线中噪声估计值时，迭代终止。将该技术与矩阵算法和直接快速傅里叶变换除法进行了比较。通过对具有不同通过时间谱和统计数据的模拟肾图数据进行反卷积来实现比较。正如预期的那样，快速傅里叶变换技术产生的结果价值不大，而CLSR和矩阵方法产生的平均通过时间（MTT）值与预期结果略有不同。分析表明，当噪声分量百分比小于6%时，矩阵方法更优，反之亦然。没有一种技术能产生有用的通过时间谱。由于在具有相对较长MTT和高噪声的模拟中，CLSR技术比矩阵方法产生了更好的结果，因此似乎有理由建议它可用于肾图反卷积，而无需事先检查曲线。
