  High-resolution diffusion tensor imaging (DTI) is beneficial for probing
tissue microstructure in fine neuroanatomical structures, but long scan times
and limited signal-to-noise ratio pose significant barriers to acquiring DTI at
sub-millimeter resolution. To address this challenge, we propose a deep
learning-based super-resolution method entitled "SRDTI" to synthesize
high-resolution diffusion-weighted images (DWIs) from low-resolution DWIs.
SRDTI employs a deep convolutional neural network (CNN), residual learning and
multi-contrast imaging, and generates high-quality results with rich textural
details and microstructural information, which are more similar to
high-resolution ground truth than those from trilinear and cubic spline
interpolation.
