Rapid Bayesian inference for perfusion quantification using ASL-MRI with a VAE-based neural network structure

A Variational Autoencoder (VAE) based framework was created to solve perfusion parameter estimation problem for ASL non-linear forward models. The ultimate goal was to build up an efficient and uncertainty-aware framework for parameter estimation problem in medical imaging, using the concept from Variaitonal Bayes (VB) which was already applied to ASL. Evaluation was performed using simulation and real data experiments with a bi-exponential model and two ASL-MRI forward models with different complexity. Compared with Markov Chain Monte Carlo (MCMC) and analytical VB (aVB), our VAE-based model achieved comparable accuracy, and hundredfold improvement in computational time.