Bayesian learning for fast parameter inference of multi-exponential white matter signals

In this work we use Bayesian learning methods to investigate data-driven approaches to parameter inference of multi-exponential white matter signals. Multi spin-echo (MSE) signals are simulated by solving the Bloch-Torrey on 2D geometries containing myelinated axons, and a conditional variational autoencoder (CVAE) model is used to learn to map simulated signals to posterior parameter distributions. This approach allows for the mapping of MSE signals directly to physical parameter vectors without expensive post-processing. We demonstrate the effectiveness of this model through the simultaneous inference of the myelin water fraction, flip angle, intra-/extracullar water $$$T_2$$$, myelin water $$$T_2$$$, and myelin g-ratio.