基于动态优化的X射线CT低剂量重建

X-ray CT low-dose reconstruction via dynamic optimization implementation

针对X射线CT成像系统在低剂量扫描协议下出现的噪声和伪影问题,提出一种基于动态优化的CT低剂量成像算法。文中使用一族双曲正切函数集构造分数阶全变分的动态复合函数模型,结合统计迭代重建框架,并通过动态优化过程实现CT低剂量扫描数据的重建。应用所提算法对数值模型和动物模型低剂量扫描生成的投影数据分别进行了重建实验。数值仿真实验结果表明,在180个投影角度下,文中算法重建结果的信噪比与滤波反投影法、分数阶全变分法、自适应变权全变分法的重建结果相比分别高出29.51,8.03,9.15,6.81 dB。动物模型实验结果表明,该算法重建结果有效抑制了CT噪声和伪影,清晰重建出软组织边界细节,极大提高了低剂量数据重建图像的质量。

A novel low-dose reconstruction algorithm using dynamic optimization design was developed to suppress artifacts and noise in computed tomography(CT)imaging under the low-dose scan protocols.In this paper,a family of hyperbolic tangent functions was selected to build the composite function model of fractional total variation(TpV).Furthermore,a dynamic optimization(DO)term was also applied to improve the performance of the presented model.In our method,the proposed iterative reconstruction was achieved via the statistical iterative reconstruction(SIR)framework.Then,the presented approach was evaluated by using X-ray low dose projections collected from simulated phantom and scanned mice.The simulated results for 180 sampling views show that the signal to noise ratios(SNR)of images reconstructed by the proposed algorithm are 29.51,8.03,9.15,6.81 dB higher than those of images reconstructed by filtered back-projection algorithm,TpV algorithm,and adaptively reweighted total variation algorithm,respectively.And the mouse data studies demonstrate that the proposed method suppresses artifacts or noise successfully and preserves more soft tissue details in reconstructed images.Moreover,the quality of reconstructed images can be greatly improved by the presented low-dose reconstruction algorithm.