Abstract #1204

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

Daan Kuppens^1,2, Sebastiano Barbieri³, Susanne Rauh^1,4, and Oliver Gurney-Champion^1,2

¹Radiology & Nuclear Medicine, Amsterdam University Medical Centers location University of Amsterdam, Amsterdam, Netherlands, ²Imaging and Biomarkers, Cancer Center Amsterdam, Amsterdam, Netherlands, ³Centre for Big Data Research in Health, University of New South Wales Sydney, Sydney, Australia, ⁴Department of Radiology, C.J. Gorter MRI Center, Leiden University Medical Center, Leiden, Netherlands

Synopsis

Keywords: Analysis/Processing, Quantitative Imaging

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.

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