Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging

Diffusion magnetic resonance imaging (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure reconstruction techniques require a large number of diffusion gradients, which is not clinically feasible due to imaging time constraints, and this has limited the use of tissue microstructure information in clinical settings. Recently, approaches based on deep learning (DL) have achieved promising tissue microstructure reconstruction results using clinically feasible dMRI. However, it remains unclear whether the subtle tissue changes associated with disease or age are properly preserved with DL approaches and whether DL reconstruction results can benefit clinical applications. Here, we provide the first evidence that DL approaches to tissue microstructure reconstruction yield reliable brain tissue microstructure analysis based on clinically feasible dMRI scans. Specifically, we reconstructed tissue microstructure from four different brain dMRI datasets with only 12 diffusion gradients, a clinically feasible protocol, and the neurite orientation dispersion and density imaging (NODDI) and spherical mean technique (SMT) models were considered. With these results we show that disease-related and age-dependent alterations of brain tissue were accurately identified. These findings demonstrate that DL tissue microstructure reconstruction can accurately quantify microstructural alterations in the brain based on clinically feasible dMRI.

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Publication Details

Journal
NeuroImage
Published
2024-09-23
DOI
https://doi.org/10.1016/j.neuroimage.2024.120858
Citations
2
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.81

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article

Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging

Yunyun Duan, Zhizheng Zhuo, Chuyang Ye, Yaou Liu et al.
2 citations
NeuroImage
Advanced Neuroimaging Techniques and Applications
0.81
article

Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging

Yunyun Duan, Zhizheng Zhuo, Chuyang Ye, Yaou Liu, Xinghu Zhang, Xiaofeng Zhang, Frederik Barkhof, Jerry L. Prince, Aaron Carass, Jiwei Jiang, De‐Cai Tian, Yanli Wang, Fu‐Dong Shi, Runzhi Li, Chenghao Liu, Yuxing Li, Yulu Shi, Jun Xu, Tingting Wang
article en
2 citations

Abstract

Diffusion magnetic resonance imaging (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure reconstruction techniques require a large number of diffusion gradients, which is not clinically feasible due to imaging time constraints, and this has limited the use of tissue microstructure information in clinical settings. Recently, approaches based on deep learning (DL) have achieved promising tissue microstructure reconstruction results using clinically feasible dMRI. However, it remains unclear whether the subtle tissue changes associated with disease or age are properly preserved with DL approaches and whether DL reconstruction results can benefit clinical applications. Here, we provide the first evidence that DL approaches to tissue microstructure reconstruction yield reliable brain tissue microstructure analysis based on clinically feasible dMRI scans. Specifically, we reconstructed tissue microstructure from four different brain dMRI datasets with only 12 diffusion gradients, a clinically feasible protocol, and the neurite orientation dispersion and density imaging (NODDI) and spherical mean technique (SMT) models were considered. With these results we show that disease-related and age-dependent alterations of brain tissue were accurately identified. These findings demonstrate that DL tissue microstructure reconstruction can accurately quantify microstructural alterations in the brain based on clinically feasible dMRI.

NeuroImageVol. 300
Beijing Institute of Technology (CN), Johns Hopkins University (US), Capital Medical University (CN), Beijing Electronic Science and Technology Institute (CN), Beijing Tian Tan Hospital (CN), Amsterdam University Medical Centers (NL), University of Amsterdam (NL), Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
National Natural Science Foundation of China, McDonnell Center for Systems Neuroscience, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 26%
Advanced Neuroimaging Techniques and Applications
0.81
DOIOpen Access PDFOpenAlex
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