Estimating uncertainty in diffusion MRI models using generative deep learning

Uncertainty is an important aspect of fitting quantitative models to diffusion MRI data, which is often overlooked. This study presents a method for estimating uncertainty intrinsic to a model using a generative deep learning approach (Denoising Diffusion Probabilistic Models (DDPM)). We numerically validate that the approach provides accurate uncertainty estimates, and demonstrate its use in providing signal-specific uncertainty estimates. Furthermore, we show that DDPM can be used as a fitting method that estimates uncertainty, and show both ADC and IVIM fitting on an in vivo brain scan. This shows promise for DDPM, as both an investigate tool and fitting method.