{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tian Q"],"funding":["NIBIB NIH HHS","NIA NIH HHS","NIMH NIH HHS","NINDS NIH HHS"],"pagination":["119033"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9511973"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["253"],"pubmed_abstract":["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 r"],"journal":["NeuroImage"],"pubmed_title":["SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI."],"pmcid":["PMC9511973"],"funding_grant_id":["U01 EB026996","P41 EB015896","K99 AG073506","U01 EB025162","R01 EB017337","R01 MH111419","K23 NS096056","P41 EB030006","R01 NS118187","R03 EB031175","U54 MH091657","U01 AG052564","R01 EB028797"],"pubmed_authors":["Polimeni JR","Li Z","Salat DH","Huang SY","Fan Q","Bilgic B","Tian Q"],"additional_accession":[]},"is_claimable":false,"name":"SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI.","description":"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 r","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jun","modification":"2025-04-21T20:04:43.93Z","creation":"2025-04-05T17:55:38.953Z"},"accession":"S-EPMC9511973","cross_references":{"pubmed":["35240299"],"doi":["10.1016/j.neuroimage.2022.119033"]}}