  Iodinated contrast media is essential for dual-energy computed tomography
(DECT) angiography. Previous studies show that iodinated contrast media may
cause side effects, and the interruption of the supply chain in 2022 led to a
severe contrast media shortage in the US. Both factors justify the necessity of
contrast media reduction in relevant clinical applications. In this study, we
propose a diffusion model-based deep learning framework to address this
challenge. First, we simulate different levels of low contrast dosage DECT
scans from the standard normal contrast dosage DECT scans using material
decomposition. Conditional denoising diffusion probabilistic models are then
trained to enhance the contrast media and create contrast-enhanced images. Our
results demonstrate that the proposed methods can generate high-quality
contrast-enhanced results even for images obtained with as low as 12.5% of the
normal contrast dosage. Furthermore, our method outperforms selected competing
methods in a human reader study.
