Acquisition-Independent Deep Learning for Quantitative MRI Parameter Estimation using Neural Controlled Differential Equations
Journal:
arXiv
Published Date:
Dec 30, 2024
Abstract
Deep learning has proven to be a suitable alternative to least-squares (LSQ)
fitting for parameter estimation in various quantitative MRI (QMRI) models.
However, current deep learning implementations are not robust to changes in MR
acquisition protocols. In practice, QMRI acquisition protocols differ
substantially between different studies and clinical settings. The lack of
generalizability and adoptability of current deep learning approaches for QMRI
parameter estimation impedes the implementation of these algorithms in clinical
trials and clinical practice. Neural Controlled Differential Equations (NCDEs)
allow for the sampling of incomplete and irregularly sampled data with variable
length, making them ideal for use in QMRI parameter estimation. In this study,
we show that NCDEs can function as a generic tool for the accurate prediction
of QMRI parameters, regardless of QMRI sequence length, configuration of
independent variables and QMRI forward model (variable flip angle T1-mapping,
intravoxel incoherent motion MRI, dynamic contrast-enhanced MRI). NCDEs
achieved lower mean squared error than LSQ fitting in low-SNR simulations and
in vivo in challenging anatomical regions like the abdomen and leg, but this
improvement was no longer evident at high SNR. NCDEs reduce estimation error
interquartile range without increasing bias, particularly under conditions of
high uncertainty. These findings suggest that NCDEs offer a robust approach for
reliable QMRI parameter estimation, especially in scenarios with high
uncertainty or low image quality. We believe that with NCDEs, we have solved
one of the main challenges for using deep learning for QMRI parameter
estimation in a broader clinical and research setting.
