  We propose a dual-domain cascade of U-nets (i.e. a "W-net") operating in both
the spatial frequency and image domains to enhance low-dose CT (LDCT) images
without the need for proprietary x-ray projection data. The central slice
theorem motivated the use of the spatial frequency domain in place of the raw
sinogram. Data were obtained from the AAPM Low-dose Grand Challenge. A
combination of Fourier space (F) and/or image domain (I) U-nets and W-nets were
trained with a multi-scale structural similarity and mean absolute error loss
function to denoise filtered back projected (FBP) LDCT images while maintaining
perceptual features important for diagnostic accuracy. Deep learning
enhancements were superior to FBP LDCT images in quantitative and qualitative
performance with the dual-domain W-nets outperforming single-domain U-net
cascades. Our results suggest that spatial frequency learning in conjunction
with image-domain processing can produce superior LDCT enhancement than
image-domain-only networks.
