Improved magnetic resonance myelin water imaging using multi-channel denoising convolutional neural networks (MCDnCNN)

Abstract

Background: Myelin water imaging (MWI) is powerful and important for studying and diagnosing neurological and psychiatric diseases. In particular, myelin water fraction (MWF) is derived from MWI data for quantifying myelination. However, MWF estimation is typically sensitive to noise. Improving the accuracy of MWF estimation based on WMI data acquired using a magnetic resonance (MR) multiple gradient recalled echo (mGRE) imaging sequence is desired. Methods:The proposed method employs a recently introduced the multi-channel denoising convolutional neural networks (MCDnCNN). Five different MCDnCNN models, denoted as Delevel1, Delevel2, Delevel3, Delevel4 and DelevelMix corresponding to five noise levels (Level1, Level2, Level3, Level4 and LevelMix), were trained using the data of the first echo of the mGRE brain images acquired from 15 healthy human subjects. Using simulated noisy data that employed a hollow cylinder model, we first evaluated the improvement in estimating MWF based on data denoised by the five different MCDnCNNs, by comparing the MWF maps calculated from the denoised data with ground truth. Next, we again evaluated the improvement using real-world in vivo datasets of 11 human participants acquired using the mGRE sequence.The datasets were first denoised by five different MCDnCNNs (Delevel1, 2, 3, 4 and DelevelMix), and subsequently their MWF maps were calculated and compared with the MWF maps directly calculated from the raw mGRE images without being denoised.Results: Experiments using the simulation data denoised by the appropriate MCDnCNN models showed that the standard deviation (SD) of the absolute error (AE) of the derived MWF results was significantly reduced (maximal reduction =15.5%, Level3 simulated noisy data, orientation angle =0, all the five MCDnCNN models). In the test using in vivo data, estimating MWF based on data particularly denoised by the appropriate MCDnCNN models was found to be the best, compared to otherwise not using the appropriate models. The results demonstrated that the appropriate MCDnCNN models may permit highquality MWF mapping, i.e., substantial reduction of random variation in estimating MWF-maps while preserving accuracy and structural details. Conclusions: Appropriate MCDnCNN models as proposed may improve both the accuracy and precision 2 Xu et al. Improved MWI using MCDnCNN

Rapid whole-brain high-resolution myelin water fraction mapping from extremely under-sampled magnetic resonance imaging data using deep neural network

“…Furthermore, emerging evidence [27][28][29][30][31][32][33][34][35][36] demonstrates that artificial NN can also drastically shorten the computational burden in parameters estimation, and was also used to derive MWF maps from a limited number of images. In recent work, Liu et al 29 have proposed deep learning NN models for rapid computation of MWF maps from conventional multiple echo-time images 37 .…”

REUSED: A deep neural network method for rapid whole-brain high-resolution myelin water fraction mapping from extremely under-sampled MRI

“…Emerging evidence demonstrates that artificial NN can also drastically alleviate the computational burden in parameter estimation and has been utilized to derive MWF maps from a limited number of images ( Jung et al, 2022 ; Kim et al, 2022 ; Liu et al, 2020 , 2022 ; Lee et al, 2020 ; Piredda et al, 2021 ; Drenthen et al, 2021 ; Xu et al, 2022 ; Jung et al, 2021 ; Khattar et al, 2021b ). In recent work, Liu et al ( Liu et al, 2020 ).…”