Generic and Robust Quantitative MRI Parameter Estimation using Neural Controlled Differential Equations

Motivation: Tissue properties are estimated from MRI data using bio-physical models that relate MRI signal to underlying tissue properties via quantitative MRI parameters. Deep learning can improve parameter estimation, but needs retraining for different acquisition protocols, hindering implementation. Goal(s): Implement a deep learning algorithm able to estimate quantitative MRI parameters for multiple quantitative MRI applications, irrespective of acquisition protocol. Approach: Neural controlled differential equations (NCDEs) overcome this limitation as they are independent of the configuration of input data. Results: NCDEs have improved performance compared to least squares minimization in estimating quantitative MRI parameters when SNR is low or when the parameter has low sensitivity. Impact: Neural controlled differential equations are a generic purpose tool for parameter estimation in quantitative MRI that outperform least squares minimization in quantitative MRI parameter estimation, irrespective of acquisition protocol or quantitative MRI application.