低剂量CT重建的可证明收敛学习不精确下降算法
Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction
摘要：
我们提出了一种可证明收敛的方法，称为有效的学习下降算法（ELDA），低剂量CT（LDCT）重建。ELDA是一种高度可解释的神经网络结构，具有学习参数，同时保留了经典优化算法的收敛保证。为了提高重建质量，建议ELDA还采用了一个新的非局部特征映射和相关的正则化。我们在一组LDCT重建问题上将ELDA与几种最先进的深度图像方法（例如RED-CNN和Learned Primal-Dual）进行了比较。数值实验表明，使用ELDA重建质量的改善，只有19层，表明ELDA的解决方案的精度和参数效率的前景表现。
英文摘要：
We propose a provably convergent method, called Efficient Learned Descent Algorithm (ELDA), for low-dose CT (LDCT) reconstruction. ELDA is a highly interpretable neural network architecture with learned parameters and meanwhile retains convergence guarantee as classical optimization algorithms. To improve reconstruction quality, the proposed ELDA also employs a new non-local feature mapping and an associated regularizer. We compare ELDA with several state-of-the-art deep image methods, such as RED-CNN and Learned Primal-Dual, on a set of LDCT reconstruction problems. Numerical experiments demonstrate improvement of reconstruction quality using ELDA with merely 19 layers, suggesting the promising performance of ELDA in solution accuracy and parameter efficiency.
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