biostudies-literatureTian QNIBIB NIH HHSNIA NIH HHSNIMH NIH HHSNINDS NIH HHS119033https://www.ebi.ac.uk/biostudies/studies/S-EPMC9511973biostudies-literatureUnknown253Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not rNeuroImageSDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI.PMC9511973U01 EB026996P41 EB015896K99 AG073506U01 EB025162R01 EB017337R01 MH111419K23 NS096056P41 EB030006R01 NS118187R03 EB031175U54 MH091657U01 AG052564R01 EB028797Polimeni JRLi ZSalat DHHuang SYFan QBilgic BTian QfalseSDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI.Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not r2022-01-01T00:00:00Z2022 Jun2025-04-21T20:04:43.93Z2025-04-05T17:55:38.953ZS-EPMC95119733524029910.1016/j.neuroimage.2022.119033