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为了抑制单光子发射断层成像(SPECT)中噪声的影响,提高重建图像的质量和定量精度,应用奇异值分解(SVD)方法进行图像重建。对人体胸腔模型进行Monte-Carlo模拟计算,生成三维SPECT系统传输矩阵和模拟投影图像,求解系统传输矩阵的广义逆矩阵。在有噪声情况下,存在最佳保留奇异值数目,使重建图像质量达到最优。最佳保留奇异值数目的不同体现了噪声的差异。与常规重建方法进行比较,SVD重建算法具有更好的噪声抑制和重建图像质量,是一种值得关注的SPECT图像重建算法。
In order to suppress the influence of noise in single photon emission tomography (SPECT) and improve the quality and quantitative accuracy of the reconstructed image, SVD (Singular Value Decomposition) method is applied to image reconstruction. The Monte Carlo simulation of the human chest model was performed to generate the three-dimensional SPECT system transmission matrix and the simulation projection image, and the generalized inverse matrix of the system transmission matrix was solved. In the presence of noise, the optimal number of reserved singular values exists, which optimizes the reconstructed image quality. The difference in the number of best-preserved singularities reflects the difference in noise. Compared with the conventional reconstruction method, the SVD reconstruction algorithm has better noise suppression and reconstruction image quality, and is a remarkable SPECT image reconstruction algorithm.