Figure 2:

Conventional methods have imposed constraints via the time evolution of a gradient flow on a Riemannian manifold to get a unique solution for the free water elimination on single-shell dMRI. Previous deep learning approaches achieve accurate model fitting for multi-shell and single-shell data. However, this framework did not allow for variations in input data size and therefore did not achieve a unified model for both data types. The prediction result shall have a significant bias when fed with dMRI from an unseen acquisition scheme. In our study, we proposed a single holistic model for different shell configurations that can recover/predict microstructural measures. Both single-shell and multi-shell dMRI sequences can be fed into the model together to improve the model performance on various shell configurations.