Low-Dose CT Reconstruction via Dual-Domain Learning and Controllable Modulation

Abstract

Existing CNN-based low-dose CT reconstruction methods focus on restoring the degraded CT images by processing on the image domain or the raw data (sinogram) domain independently, or leveraging both domains by connecting them through some simple domain transform operators or matrices. However, both domains and their mutual benefits are not fully exploited, which impedes the performance to go step further. In addition, considering the subjective perceptual quality of the restored image, it is more necessary for doctors to adaptively control the denoising level for different regions or organs according to diagnosis convenience, which cannot be done using existing deterministic networks. To tackle these difficulties, this paper breaks away the shackles of general paradigms and proposes a novel low-dose CT reconstruction framework via dual-domain learning and controllable modulation. Specifically, we propose a dual-domain base network to fully address the mutual dependencies between the image domain and sinogram domain. Upon this, we integrate a controllable modulation module to adjust the latent features of the base network, which allows to finely-grained control the reconstruction by considering the trade-off between artifacts reduction and detail preservation to assist doctors in diagnosis. Experiments results on Mayo clinic dataset and Osaka dataset demonstrate that our method achieves superior performance.