A C-GAN denoising algorithm in Projection Domain for Micro-CT

Abstract

Micro-CT provides a high-resolution 3D imaging of micro-architecture in a non-invasive way, which becomes a significant tool in biomedical research and preclinical applications. Due to the limited power of micro-focus X-ray tube, photon starving occurs and noise is inevitable for the projection images, resulting in the degradation of spatial resolution, contrast and image details. In this paper, we propose a C-GAN (Conditional Generative Adversarial Nets) denoising algorithm in projection domain for Micro-CT imaging. The noise statistic property is utilized directly and a novel variance loss is developed to suppress the blurry effects during denoising procedure. Conditional Generative Adversarial Networks (C-GAN) is employed as a framework to implement the denoising task. To guarantee the pixelwised accuracy, fully convolutional network is served as the generator structure. During the alternative training of the generator and the discriminator, the network is able to learn noise distribution automatically. Moreover, residual learning and skip connection architecture are applied for faster network training and further feature fusion. To evaluate the denoising performance, mouse lung, milkvetch root and bamboo stick are imaged by micro-CT in the experiments. Compared with BM3D, CNN-MSE and CNN-VGG, the proposed method can suppress noise effectively and recover image details without introducing any artifacts or blurry effect. The result proves that our method is feasible, efficient and practical.