Bibliographic Details
| Title: |
Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging |
| Authors: |
Yuxing Li, Zhizheng Zhuo, Chenghao Liu, Yunyun Duan, Yulu Shi, Tingting Wang, Runzhi Li, Yanli Wang, Jiwei Jiang, Jun Xu, Decai Tian, Xinghu Zhang, Fudong Shi, Xiaofeng Zhang, Aaron Carass, Frederik Barkhof, Jerry L Prince, Chuyang Ye, Yaou Liu |
| Source: |
NeuroImage, Vol 300, Iss , Pp 120858- (2024) |
| Publisher Information: |
Elsevier, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Neurosciences. Biological psychiatry. Neuropsychiatry |
| Subject Terms: |
Diffusion magnetic resonance imaging, Deep learning, Tissue microstructure reconstruction, Clinical brain analysis, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1095-9572 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S1053811924003550; https://doaj.org/toc/1095-9572 |
| DOI: |
10.1016/j.neuroimage.2024.120858 |
| Access URL: |
https://doaj.org/article/2b52606b9adc482e9d825471ede16ca7 |
| Accession Number: |
edsdoj.2b52606b9adc482e9d825471ede16ca7 |
| Database: |
Directory of Open Access Journals |
