Computed tomography (CT) imaging is a widely used modality for early lung
cancer diagnosis, treatment, and prognosis. Features extracted from CT images
are now accepted to quantify spatial and temporal variations in tumor
architecture and function. However, CT images are often acquired using scanners
from different vendors with customized acquisition standards, resulting in
significantly different texture features even for the same patient, posing a
fundamental challenge to downstream studies. Existing CT image harmonization
models rely on supervised or semi-supervised techniques, with limited
performance. In this paper, we have proposed a diffusion-based CT image
standardization model called DiffusionCT which works on latent space by mapping
latent distribution into a standard distribution. DiffusionCT incorporates an
Unet-based encoder-decoder and a diffusion model embedded in its bottleneck
part. The Unet first trained without the diffusion model to learn the latent
representation of the input data. The diffusion model is trained in the next
training phase. All the trained models work together on image standardization.
The encoded representation outputted from the Unet encoder passes through the
diffusion model, and the diffusion model maps the distribution in to target
standard image domain. Finally, the decode takes that transformed latent
representation to synthesize a standardized image. The experimental results
show that DiffusionCT significantly improves the performance of the
standardization task.Comment: 6 pages, 03 figures and 01 table