  The progression of Idiopathic Pulmonary Fibrosis (IPF) significantly
correlates with higher patient mortality rates. Early detection of IPF
progression is critical for initiating timely treatment, which can effectively
slow down the advancement of the disease. However, the current clinical
criteria define disease progression requiring two CT scans with a one-year
interval, presenting a dilemma: a disease progression is identified only after
the disease has already progressed. To this end, in this paper, we develop a
novel diffusion model to accurately predict the progression of IPF by
generating patient's follow-up CT scan from the initial CT scan. Specifically,
from the clinical prior knowledge, we tailor improvements to the traditional
diffusion model and propose a Clinically-Informed Residual Diffusion model,
called CIResDiff. The key innovations of CIResDiff include 1) performing the
target region pre-registration to align the lung regions of two CT scans at
different time points for reducing the generation difficulty, 2) adopting the
residual diffusion instead of traditional diffusion to enable the model focus
more on differences (i.e., lesions) between the two CT scans rather than the
largely identical anatomical content, and 3) designing the clinically-informed
process based on CLIP technology to integrate lung function information which
is highly relevant to diagnosis into the reverse process for assisting
generation. Extensive experiments on clinical data demonstrate that our
approach can outperform state-of-the-art methods and effectively predict the
progression of IPF.
