Input layer regularization and automated regularization hyperparameter tuning for myelin water estimation using deep learning

TL;DR

This paper introduces a deep learning approach that combines classical regularization and data augmentation for improved myelin water fraction estimation in brain MRI, with automated hyperparameter tuning and superior performance over traditional methods.

Contribution

It extends input layer regularization with optimal parameter selection and integrates MWF estimation into deep learning for biexponential analysis.

Findings

Deep learning outperforms classical methods on synthetic data.

The architecture achieves better results on in vivo brain data.

GCV-based regularization parameter selection is slightly superior to neural network selection.

Abstract

We propose a novel deep learning method which combines classical regularization with data augmentation for estimating myelin water fraction (MWF) in the brain via biexponential analysis. Our aim is to design an accurate deep learning technique for analysis of signals arising in magnetic resonance relaxometry. In particular, we study the biexponential model, one of the signal models used for MWF estimation. We greatly extend our previous work on \emph{input layer regularization (ILR)} in several ways. We now incorporate optimal regularization parameter selection via a dedicated neural network or generalized cross validation (GCV) on a signal-by-signal, or pixel-by-pixel, basis to form the augmented input signal, and now incorporate estimation of MWF, rather than just exponential time constants, into the analysis. On synthetically generated data, our proposed deep learning architecture outperformed both classical methods and a conventional multi-layer perceptron. On in vivo brain data, our architecture again outperformed other comparison methods, with GCV proving to be somewhat superior to a NN for regularization parameter selection. Thus, ILR improves estimation of MWF within the biexponential model. In addition, classical methods such as GCV may be combined with deep learning to optimize MWF imaging in the human brain.