基于迭代p阈值算法压缩感知磁共振成像重构

Reconstruction Network of Compressed Sensing Magnetic Resonance Imaging Based on Iterative P-threshold Algorithm

从优化网络结构出发,在基于迭代软阈值网络的压缩感知磁共振成像深度网络基础上,加入由p阈值函数组成的优化模块,进一步优化软阈值函数,以抑制噪声,减少重建误差,从而提高重建质量。上述算法结合了压缩感知磁共振重建和深度学习的优势,所有参数都是端到端学习得到的,既具有很好的理论可解释性,又具有良好的网络泛化能力。对上述算法与其它算法进行对比,仿真结果表明,所提算法提高了磁共振成像的重建精度,特别对于结构复杂的磁共振图像重建效果更好。

Based on the deep network of compressed sensing magnetic resonance imaging based on iterative soft threshold network,an optimization module composed of P-threshold function was added to further optimize the soft threshold function to suppress noise and reduce reconstruction error,so as to improve reconstruction quality.The algorithm combines the advantages of compressed sensing magnetic resonance reconstruction and deep learning,and all parameters are learned end-to-end,which has good theoretical interpretability and good network generalization ability.Compared with other algorithms,the simulation results show that the proposed algorithm improves the reconstruction accuracy of magnetic resonance imaging.