  Learning-based approaches, especially those based on deep networks, have
enabled high-quality estimation of tissue microstructure from low-quality
diffusion magnetic resonance imaging (dMRI) scans, which are acquired with a
limited number of diffusion gradients and a relatively poor spatial resolution.
These learning-based approaches to tissue microstructure estimation require
acquisitions of training dMRI scans with high-quality diffusion signals, which
are densely sampled in the q-space and have a high spatial resolution. However,
the acquisition of training scans may not be available for all datasets.
Therefore, we explore knowledge transfer between different dMRI datasets so
that learning-based tissue microstructure estimation can be applied for
datasets where training scans are not acquired. Specifically, for a target
dataset of interest, where only low-quality diffusion signals are acquired
without training scans, we exploit the information in a source dMRI dataset
acquired with high-quality diffusion signals. We interpolate the diffusion
signals in the source dataset in the q-space using a dictionary-based signal
representation, so that the interpolated signals match the acquisition scheme
of the target dataset. Then, the interpolated signals are used together with
the high-quality tissue microstructure computed from the source dataset to
train deep networks that perform tissue microstructure estimation for the
target dataset. Experiments were performed on brain dMRI scans with low-quality
diffusion signals, where the benefit of the proposed strategy is demonstrated.
