KBA-PDNet: A primal-dual unrolling network with kernel basis attention for low-dose CT reconstruction

Abstract

Computed tomography (CT) image reconstruction is faced with challenge of balancing image quality and radiation dose. Recent unrolled optimization methods address low-dose CT image quality issues using convolutional neural networks or self-attention mechanisms as regularization operators. However, these approaches have limitations in adaptability, computational efficiency, or preservation of beneficial inductive biases. They also depend on initial reconstructions, potentially leading to information loss and error propagation. To overcome these limitations, Kernel Basis Attention Primal-Dual Network (KBA-PDNet) is proposed. The method unrolls multiple iterations of the proximal primal-dual optimization process, replacing traditional proximal operators with Kernel Basis Attention (KBA) modules. This design enables direct training from raw measurement data without relying on preliminary reconstructions. The KBA module achieves adaptability by learning and dynamically fusing kernel bases, generating customized convolution kernels for each spatial location. This approach maintains computational efficiency while preserving beneficial inductive biases of convolutions. By training end-to-end from raw projection data, KBA-PDNet fully utilizes all original information, potentially capturing details lost in preliminary reconstructions. Experiments on simulated and clinical datasets demonstrate that KBA-PDNet outperforms existing approaches in both image quality and computational efficiency.