A denoising diffusion fluid flow generative model for stenotic pipe flows

Generative models have been used to combat medical image scarcity by generating new samples to increase the size of the training data. The hypothesis of this paper is that Denoising Diffusion Probabilistic Models (DDPM), have the ability to synthesize high quality flow images and potentially obviate the need to rely on time-consuming CFD simulations. As a first step, in this paper, we concentrate on data generation at only the peak flow rate of the simulation and propose a DDPM that, with high accuracy, can generate velocity fields for many unseen flow rates in a fixed in-vitro phantom geometry with rigid walls modeling a vascular stenosis.