Abstract

Computed tomography (CT) is indispensable in both clinical medicine and biological research, yet reducing radiation exposure while maintaining image quality remains a big challenge. To address this, we propose multi-Gaussian Cluster Variance Reduction (mGCVR), a method that enables low-dose CT images to approximate the quality of high-dose scans. mGCVR models the heterogeneous tissue CT intensity distribution using multiple Gaussian components, and performs denoising by shrinking the variance within each component.

In biological imaging experiments, mGCVR consistently improves image quality across the entire field of view. Compared with classical denoising algorithms, mGCVR produces images that more closely resemble high-dose clinical CT images and achieves superior performance in quantitative metrics. These results validate the effectiveness of mGCVR and highlight its potential for broad use in both medical imaging and scientific applications.