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Learning-based approaches, especially those based on deep networks, haveenabled high-quality estimation of tissue microstructure from low-qualitydiffusion magnetic resonance imaging (dMRI) scans, which are acquired with alimited number of diffusion gradients and a relatively poor spatial resolution.These learning-based approaches to tissue microstructure estimation requireacquisitions of training dMRI scans with high-quality diffusion signals, whichare 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 sothat learning-based tissue microstructure estimation can be applied fordatasets where training scans are not acquired. Specifically, for a targetdataset of interest, where only low-quality diffusion signals are acquiredwithout training scans, we exploit the information in a source dMRI datasetacquired with high-quality diffusion signals. We interpolate the diffusionsignals in the source dataset in the q-space using a dictionary-based signalrepresentation, so that the interpolated signals match the acquisition schemeof the target dataset. Then, the interpolated signals are used together withthe high-quality tissue microstructure computed from the source dataset totrain deep networks that perform tissue microstructure estimation for thetarget dataset. Experiments were performed on brain dMRI scans with low-qualitydiffusion signals, where the benefit of the proposed strategy is demonstrated.

Knowledge Transfer between Datasets for Learning-based Tissue Microstructure Estimation