Fast MR signal simulations of microvascular and diffusion contributions using histogram-based approximation and recurrent neural networks.

Purpose: Accurate MR signal simulation, including microvascular structures and water diffusion, is crucial for MRI techniques like fMRI BOLD modeling and MR vascular Fingerprinting (MRF), which use susceptibility effects on MR signals for tissue characterization. However, integrating microvascular features and diffusion remains computationally challenging, limiting the accuracy of the estimates. Using advanced modeling and deep neural networks, we propose a novel simulation tool that efficiently accounts for susceptibility and diffusion effects.

Methods: We used dimension reduction of magnetic field inhomogeneity matrices combined with deep learning methodology to accelerate the simulations while maintaining their accuracy. We validated our results through an in silico study against a reference method and in vivo MRF experiments.

Results: This approach accelerates MR signal generation by a factor of almost 13 000 compared to previously used simulation methods while preserving accuracy.

Conclusion: The MR-WAVES method allows fast generation of MR signals accounting for microvascular structures and water-diffusion contribution.