# Knowledge Transfer between Datasets for Learning-based Tissue Microstructure Estimation

arXiv: https://arxiv.org/abs/1910.10930 · HTML: https://mlsanity.com/paper/1910.10930/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review](https://mlsanity.com/paper/2102.01530/) (2021)
- [A microstructure estimation Transformer inspired by sparse representation for diffusion MRI](https://mlsanity.com/paper/2205.06450/) (2022)
- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)

## Abstract (arXiv)

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.