Low-Dose CT Image Super-Resolution Network with Dual-Guidance Feature Distillation and Dual-Path Content Communication

Low-dose computer tomography (LDCT) has been widely used in medical diagnosis yet suffered from spatial resolution loss and artifacts. Numerous methods have been proposed to deal with those issues, but there still exists drawbacks: (1) convolution without guidance causes essential information not highlighted; (2) features with fixed-resolution lose the attention to multi-scale information; (3) single super-resolution module fails to balance details reconstruction and noise removal. Therefore, we propose an LDCT image super-resolution network consisting of a dual-guidance feature distillation backbone for elaborate visual feature extraction, and a dual-path content communication head for artifacts-free and details-clear CT reconstruction. Specifically, the dual-guidance feature distillation backbone is composed of a dual-guidance fusion module (DGFM) and a sampling attention block (SAB). The DGFM guides the network to concentrate the feature representation of the 3D inter-slice information in the region of interest (ROI) by introducing the average CT image and segmentation mask as complements of the original LDCT input. Meanwhile, the elaborate SAB utilizes the essential multi-scale features to capture visual information more relative to edges. The dual-path reconstruction architecture introduces the denoising head before and after the super-resolution (SR) head in each path to suppress residual artifacts, respectively. Furthermore, the heads with the same function share the parameters so as to efficiently improve the reconstruction performance by reducing the amount of parameters. The experiments compared with 6 state-of-the-art methods on 2 public datasets prove the superiority of our method. The code is made available at https://github.com/neu-szy/dual-guidance_LDCT_SR .