SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 253; p. 119033
• Main Authors: Tian, Qiyuan, Li, Ziyu, Fan, Qiuyun, Polimeni, Jonathan R., Bilgic, Berkin, Salat, David H., Huang, Susie Y.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2022; Elsevier Limited; Elsevier
• Subjects: Aging; Algorithms; Alzheimer's disease; Brain mapping; Convolutional neural network; Datasets; Deep Learning; Diffusion Tensor Imaging - methods; Diffusion tensor transformation; Humans; Image Processing, Computer-Assisted - methods; Image synthesis; Life span; Magnetic resonance imaging; Medical imaging; Methods; Neural networks; Neural Networks, Computer; Neuroimaging; Noise; Normal aging; Residual learning; Signal-To-Noise Ratio; Substantia alba; Supervised learning; Tomography; Training; Diffusion tensor transformation; Image synthesis; Residual learning; Supervised learning; Normal aging; Convolutional neural network
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.119033

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 require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.