Low-dose CT Denoising with Language-engaged Dual-space Alignment: Enhancing LDCT Denoising Models with LEDA

The author proposes the Language-Engaged Dual-space Alignment (LEDA) loss to optimize low-dose CT denoising models by aligning denoised CT and normal dose CT images in both continuous perceptual space and discrete semantic space.