Self-labelled encoder-decoder (SLED) for multi-echo gradient echo-based myelin water imaging

• Published in: NeuroImage (Orlando, Fla.) Vol. 264; p. 119717
• Main Authors: Liu, Hanwen, Grouza, Vladimir, Tuznik, Marius, Siminovitch, Katherine A., Bagheri, Hooman, Peterson, Alan, Rudko, David A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2022; Elsevier Limited; Elsevier
• Subjects: Animals; Brain; Brain - diagnostic imaging; Brain mapping; Deep learning; Genotypes; Gradient echo MRI; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mice; Myelin Sheath; Myelin water imaging; Myelination; Neural networks; Neuroimaging; Parameter estimation; Quantitative MRI; Substantia alba; Unsupervised learning; Water; White Matter - diagnostic imaging; Deep learning; Gradient echo MRI; Quantitative MRI; Neural networks; Myelin water imaging; Unsupervised learning
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.119717

•An unsupervised ML approach, SLED, was developed for T2* MWI data analysis.•SLED outperformed NLLS for MWF estimation using simulated phantom and real data.•SLED MWF sensitively measured graded hypomyelination in a novel KO mouse model.•SLED was efficiently trained on each dataset without the need for a signal library. Reconstruction of high quality myelin water imaging (MWI) maps is challenging, particularly for data acquired using multi-echo gradient echo (mGRE) sequences. A non-linear least squares fitting (NLLS) approach has often been applied for MWI. However, this approach may produce maps with limited detail and, in some cases, sub-optimal signal to noise ratio (SNR), due to the nature of the voxel-wise fitting. In this study, we developed a novel, unsupervised learning method called self-labelled encoder-decoder (SLED) to improve gradient echo-based MWI data fitting. Ultra-high resolution, MWI data was collected from five mouse brains with variable levels of myelination, using a mGRE sequence. Imaging data was acquired using a 7T preclinical MRI system. A self-labelled, encoder-decoder network was implemented in TensorFlow for calculation of myelin water fraction (MWF) based on the mGRE signal decay. A simulated MWI phantom was also created to evaluate the performance of MWF estimation. Compared to NLLS, SLED demonstrated improved MWF estimation, in terms of both stability and accuracy in phantom tests. In addition, SLED produced less noisy MWF maps from high resolution MR microscopy images of mouse brain tissue. It specifically resulted in lower noise amplification for all mouse genotypes that were imaged and yielded mean MWF values in white matter ROIs that were highly correlated with those derived from standard NLLS fitting. Lastly, SLED also exhibited higher tolerance to low SNR data. Due to its unsupervised and self-labeling nature, SLED offers a unique alternative to analyze gradient echo-based MWI data, providing accurate and stable MWF estimations.