Quantitative MRI Parameter Estimation using Neural Controlled Differential Equations: Proof-of-Concept in Intra-voxel Incoherent Motion

In quantitative MRI, tissue properties are estimated from MRI data using bio-physical models that relate the MRI signal to the underlying tissue properties via model parameters. Deep learning can improve parameter estimation, but is conventionally dependent on the input being either a fixed set of input signals or a series of regularly sampled signals. Neural controlled differential equations (NCDEs) are models that are independent of the configuration of input data. NCDEs have similar performance to state-of-the-art acquisition-specific deep learning methods in estimating intra-voxel incoherent motion parameters. Therefore, NCDEs are a generic purpose tool for parameter estimation in quantitative MRI.