Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction
This work addresses image quality issues in medical imaging for low-dose CT, offering an interpretable and efficient solution, though it appears incremental as it builds on existing optimization and deep learning approaches.
The authors tackled low-dose CT reconstruction by proposing ELDA, a provably convergent neural network method that improves reconstruction quality with only 19 layers, achieving better results than state-of-the-art deep learning methods like RED-CNN and Learned Primal-Dual.
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.