  The noise in diffusion-weighted images (DWIs) decreases the accuracy and
precision of diffusion tensor magnetic resonance imaging (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 practical feasibility. 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, each consisting of six DWI
volumes along optimally chosen diffusion-encoding directions that are robust to
noise for the tensor fitting, and then synthesizes DWI volumes along all
acquired directions from the diffusion tensors fitted using each subset of the
data as the input data of CNNs. On the other hand, SDnDTI synthesizes DWI
volumes along acquired diffusion-encoding directions with higher SNR from the
diffusion tensors fitted using all acquired data as the training target. SDnDTI
removes noise from each subset of synthesized DWI volumes using a deep
3-dimensional CNN to match the quality of the cleaner target DWI volumes and
achieves even higher SNR by averaging all subsets of denoised data. The
denoising efficacy of SDnDTI is demonstrated on two datasets 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.
