小波先验在OSL重建算法中的应用

背景：MAP(最大后验)统计重建方法可以在重建过程中引入合适的先验知识达到去除噪声的目的。 目的：根据小波系数的统计特性及能量平衡的原理对高频信息做相应的处理,并将多尺度的小波先验应用到OSL重建算法中以去除噪声。 方法：实验从“变换域”的思想出发,在小波域上根据小波系数的统计特性及能量平衡原理对不同尺度的高频信息做相应的处理,并利用处理后的小波系数进行小波重建。 结果与结论：基于小波先验的OSL算法比ML-EM算法重建的图像与测试模型的误差变小、相关性变大、噪声变少,重建图像变得比较平滑,视觉效果较清楚。

Application of wavelet prior in OSL reconstruction algorithm

BACKGROUND:MAP (maximum a posteriori) statistical reconstruction methods can introduce appropriate prior knowledge in reconstruction process to remove the noises. OBJECTIVE: To process the corresponding high frequency information, according to statistic characteristics of wavelet coefficients and energy balance principle, and to apply multi-scale wavelet prior to OSL reconstruction algorithm to remove noises. METHODS:Most prior information can reduce noise in airspace. From “transform domain”, according to statistic characteristics of wavelet coefficients and energy balance principle of different scales, high frequency information is processed in wavelet domain. And processed wavelet coefficients are applied in wavelet reconstruction as a kind of multi-scale wavelet prior information to remove noises. RESULTS AND CONCLUSION: The errors and noises of reconstructed images and testing model of OSL algorithm based on wavelet priori information is less than ML-EM algorithm, and correlation greater. Reconstructed images have become smoother, and visual effect is clearer. This shows that wavelet prior, based on wavelet coefficient statistical characteristic and energy balance principle, can effectively reduce the noises in CT reconstruction process.