Low-Dose CT Image Reconstruction Based on a Residual Network

Computed Tomography (CT) scanning is a common medical imaging examination method, but high doses of X-ray radiation may cause radiation damage to patients. Low-dose CT technology aims to reduce the radiation dose received by patients, reducing patient risk while maintaining image quality. However, the quality of low-dose CT images is often poor, with potential issues such as noise and artifacts. Using residual networks for image reconstruction is an effective deep learning method. By learning the residual information of images, residual networks can efficiently generate high-quality reconstructed images. This article describes the application of residual networks in the field of low-dose CT image reconstruction and summarizes their advantages and challenges. Research indicates that residual networks can reduce information loss, preserve the details of the original images, and have good universality and adaptability. Future research can further explore deeper network structures, integrate attention mechanisms, to promote the application of residual networks in low-dose CT image reconstruction.