Fast Probabilistic Parameter Estimation for Quantitative MRI using Variational Autoencoders

Motivation: Mapping MRI signal into tissue parameters aims to identify robust physiologic-phenotypic associations. However, conventional methods are computationally expensive, limiting their applicability in research or clinical practice. Goal(s): To develop fast and robust techniques for estimating quantitative tissue parameters under a probabilistic framework using MRI signals. Approach: Train and evaluate the performance of variational autoencoders and compare its capabilities with state-of-the-art deep learning methods on both synthesized and real MRI data. Results: Compared to existing autoencoder-based methods, both synthetic and real data experiments show enhanced performance of VAEs on tissue parameter estimation. Parameter maps produced from real data show higher similarity to gold-standard maps. Impact: We show that variational autoencoders can be trained for fast inference of quantitative parameter estimation MRI data quantification in qMRI.