Kadrmas Dan J, Frey Eric C, Tsui Benjamin M W
Department of Biomedical Engineering, University of North Carolina-Chapel Hill, Chapel Hill, N.C. 27599.
IEEE Trans Nucl Sci. 1996 Aug 1;43(3):2275-2284. doi: 10.1109/23.531892.
In this work singular value decomposition (SVD) techniques are used to investigate how the use of low energy photons and multiple energy windows affects the noise properties of Tc-99m SPECT imaging. We have previously shown that, when modeling scatter in the projector and backprojector of iterative reconstruction algorithms, simultaneous reconstruction from multiple energy window data can result in very different noise characteristics. Further, the properties depend upon the width and number of energy windows used. To investigate this further, we have generated photon transport matrices using models for scatter, an elliptical phantom containing cold rods of various sizes, and a number of multiple energy window acquisition schemes. Transfer matrices were also generated for the cases of perfect scatter rejection and ideal scatter subtraction. The matrices were decomposed using SVD, and signal power and projection space variance spectra were computed using the basis formed by the left singular vectors. Results indicate very different noise levels for the various energy window combinations. The perfect scatter rejection case resulted in the lowest variance spectrum, and reconstruction-based scatter compensation performed better than the scatter subtraction case. When including lower energy photons in reconstruction-based scatter compensation, using a series of multiple energy windows outperformed a single large energy window. One multiple window combination is presented which achieves a lower variance spectrum than the standard 20% energy window, indicating the potential for using low energy photons to improve the noise characteristics of SPECT images.
在这项工作中，奇异值分解（SVD）技术被用于研究低能光子的使用和多能窗对Tc-99m单光子发射计算机断层显像（SPECT）成像噪声特性的影响。我们之前已经表明，在对迭代重建算法的投影器和反投影器中的散射进行建模时，从多能窗数据同时进行重建会导致非常不同的噪声特征。此外，这些特性取决于所使用的能窗宽度和数量。为了进一步研究这一点，我们使用散射模型、包含各种尺寸冷棒的椭圆形体模以及一些多能窗采集方案生成了光子传输矩阵。还针对完全散射剔除和理想散射减法的情况生成了转移矩阵。使用SVD对矩阵进行分解，并使用由左奇异向量形成的基来计算信号功率和投影空间方差谱。结果表明，各种能窗组合的噪声水平非常不同。完全散射剔除的情况导致方差谱最低，基于重建的散射补偿比散射减法的情况表现更好。在基于重建的散射补偿中纳入低能光子时，使用一系列多能窗比使用单个大的能窗表现更好。给出了一种多窗组合，其方差谱低于标准的20%能窗，表明使用低能光子改善SPECT图像噪声特性的潜力。
