  Computed tomography (CT) serves as an effective tool for lung cancer
screening, diagnosis, treatment, and prognosis, providing a rich source of
features to quantify temporal and spatial tumor changes. Nonetheless, the
diversity of CT scanners and customized acquisition protocols can introduce
significant inconsistencies in texture features, even when assessing the same
patient. This variability poses a fundamental challenge for subsequent research
that relies on consistent image features. Existing CT image standardization
models predominantly utilize GAN-based supervised or semi-supervised learning,
but their performance remains limited. We present DiffusionCT, an innovative
score-based DDPM model that operates in the latent space to transform disparate
non-standard distributions into a standardized form. The architecture comprises
a U-Net-based encoder-decoder, augmented by a DDPM model integrated at the
bottleneck position. First, the encoder-decoder is trained independently,
without embedding DDPM, to capture the latent representation of the input data.
Second, the latent DDPM model is trained while keeping the encoder-decoder
parameters fixed. Finally, the decoder uses the transformed latent
representation to generate a standardized CT image, providing a more consistent
basis for downstream analysis. Empirical tests on patient CT images indicate
notable improvements in image standardization using DiffusionCT. Additionally,
the model significantly reduces image noise in SPAD images, further validating
the effectiveness of DiffusionCT for advanced imaging tasks.
