Comparison of two deep learning models for contrast agent dose reduction in dynamic contrast enhanced breast MRI

Motivation: In the context of MRI-based breast cancer screening, reducing contrast agent dose is desirable. However, this yields decreased contrast-to-noise ratio in dynamic-contrast-enhanced subtraction images. Goal(s): This work aimed to compare two deep learning techniques, diffusion probabilistic models (DDPM) and general adversarial networks (GAN), for retrospective contrast enhancement of low-dose breast MRI subtraction images. Approach: Training and testing was performed on virtual low dose subtraction images, which we generated by subjecting original subtraction images to different amounts of noise. Results: Both DDPM and GAN could denoise these images; however, neither model was superior over the other across all tested dose levels and evaluation metrics. Impact: Diffusion probabilistic models and general adversarial networks can retrospectively enhance the signal of virtual low-dose images. They may supplement imaging with reduced doses in the future; yet, further development and validation on real low dose images are warranted.