Hull D M, Peskin C S, Rabinowitz A M, Wexler J P, Blaufox M D
Medical Scientist Training Program, Albert Einstein College of Medicine, Bronx, NY 10461.
Phys Med Biol. 1990 Dec;35(12):1641-62. doi: 10.1088/0031-9155/35/12/005.
A non-stationary optimal smoothing filter for digital nuclear medicine image data, degraded by Poisson noise, has been derived and applied to temporal simulated and clinical gated blood pool study (GBPS) data. The derived filter is automatically calculated from a large group (library) of similar GBPS which are representative of all studies acquired according to the same protocol in a defined patient population (the ensemble). The filter is designed to minimize the mean-square difference between the filtered data and the true image values; it provides an optimal trade-off between noise reduction and signal degradation for members of the ensemble. The filter is evaluated using a computer simulated ensemble of GBPS. Libraries of Poisson-degraded and non-degraded studies were generated. Libraries of up to 400 Poisson-degraded simulated studies were used to estimate optimal temporal filters that, when applied to Poisson-degraded members of the ensemble not included in the libraries, reduced the mean-square error in the raw data by 65%. When the non-degraded studies were used instead to compute the optimal filter values, the corresponding reduction in the error was 83%. Libraries of previously acquired clinical GBPS were then used to estimate optimal temporal filters for an ensemble of similarly acquired studies. These filters were subsequently applied to studies of 13 patients (not in the original libraries) who received multiple sequential repeat studies. Comparisons of both the filtered and raw data to averages of the repeat studies demonstrated that optimal filters calculated from 400 and 800 clinical studies reduced the mean-square error in the clinical data by 56% and 63% respectively.
一种针对因泊松噪声而退化的数字核医学图像数据的非平稳最优平滑滤波器已被推导出来，并应用于时间模拟和临床门控心血池研究（GBPS）数据。所推导的滤波器是根据一大组（库）相似的GBPS自动计算得出的，这些GBPS代表了在特定患者群体中按照相同方案获取的所有研究（总体）。该滤波器旨在使滤波后的数据与真实图像值之间的均方差异最小化；它为总体中的成员在降噪和信号退化之间提供了最佳平衡。使用GBPS的计算机模拟总体对该滤波器进行评估。生成了泊松退化和未退化研究的库。使用多达400个泊松退化模拟研究的库来估计最优时间滤波器，当将其应用于库中未包含的总体的泊松退化成员时，可将原始数据中的均方误差降低65%。当使用未退化研究来计算最优滤波器值时，相应的误差降低为83%。然后使用先前获取的临床GBPS库来估计一组类似获取研究的最优时间滤波器。这些滤波器随后应用于13名患者（不在原始库中）的研究，这些患者接受了多次连续重复研究。将滤波后的数据和原始数据与重复研究的平均值进行比较表明，从400项和800项临床研究计算出的最优滤波器分别将临床数据中的均方误差降低了56%和63%。
