Keywords: Microstructure, Microstructure, self-superivsed, physics-informed
Motivation: Diffusion modeling is an important tool for quantifying microstructure properties from diffusion data, but its optimization is computationaly expensive.
Goal(s): To achieve rapid microstructure model parameter estimation while outperforming conventional methods.
Approach: DIMOND employs a neural network (NN) to map input diffusion data to model parameters and optimizes NN by minimizing the difference between the input data and the synthetic data generated via the diffusion model parametrized by NN outputs.
Results: DIMOND outperforms conventional methods for fitting kurtosis and NODDI models in terms of metric accuracy. DIMOND reduces NODDI model fitting time from hours to minutes, or even seconds by leveraging transfer learning.
Impact: DIMOND has a high potential to transform diffusion model fitting. Its self-supervised training paradigm, high efficacy and efficiency may dramatically improve the feasibility and accessibility of diffusion MRI based microstructure and connectivity mapping in clinical and neuroscientific applications.
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