LOW-DOSE CT RECONSTRCTION VIA OPTIMIZATION-INSPIRED GAN

Most research on Low-dose Computed Tomography (LDCT) reconstruction is designed as a black box, lacking controllability and interpretability. In this paper, a Proximal Linear ADMM framework-based Generative Adversarial Network (PLA-GAN) is proposed. Specifically, without loss of interpretability, channel attention blocks and NonLocal Sparse Attention (NLSA) modules are embedded into two regularizers respectively and iterated alternately, driving the network to cope with real and complex CT image degradation through a multi-scale and adaptive way. To further promote the visual quality, a discriminator containing NLSA module is also introduced. The comparisons with state-of-the-arts on the Mayo dataset validate the superiority of our proposed algorithm both numerically and visually. The advantages of generalizability and interpretability are also evident.