  Computed tomography (CT) is one of the modalities for effective lung cancer
screening, diagnosis, treatment, and prognosis. The features extracted from CT
images are now used to quantify spatial and temporal variations in tumors.
However, CT images obtained from various scanners with customized acquisition
protocols may introduce considerable variations in texture features, even for
the same patient. This presents a fundamental challenge to downstream studies
that require consistent and reliable feature analysis. Existing CT image
harmonization models rely on GAN-based supervised or semi-supervised learning,
with limited performance. This work addresses the issue of CT image
harmonization using a new diffusion-based model, named DiffusionCT, to
standardize CT images acquired from different vendors and protocols.
DiffusionCT operates in the latent space by mapping a latent non-standard
distribution into a standard one. DiffusionCT incorporates an Unet-based
encoder-decoder, augmented by a diffusion model integrated into the bottleneck
part. The model is designed in two training phases. The encoder-decoder is
first trained, without embedding the diffusion model, to learn the latent
representation of the input data. The latent diffusion model is then trained in
the next training phase while fixing the encoder-decoder. Finally, the decoder
synthesizes a standardized image with the transformed latent representation.
The experimental results demonstrate a significant improvement in the
performance of the standardization task using DiffusionCT.
