  Diffusion imaging is an important method in the field of neuroscience, as it
is sensitive to changes within the tissue microstructure of the human brain.
However, a major challenge when using MRI to derive quantitative measures is
that the use of different scanners, as used in multi-site group studies,
introduces measurement variability. This can lead to an increased variance in
quantitative metrics, even if the same brain is scanned.
  Contrary to the assumption that these characteristics are comparable and
similar, small changes in these values are observed in many clinical studies,
hence harmonization of the signals is essential.
  In this paper, we present a method that does not require additional
preprocessing, such as segmentation or registration, and harmonizes the signal
based on a deep learning residual network. For this purpose, a training
database is required, which consist of the same subjects, scanned on different
scanners.
  The results show that harmonized signals are significantly more similar to
the ground truth signal compared to no harmonization, but also improve in
comparison to another deep learning method. The same effect is also
demonstrated in commonly used metrics derived from the diffusion MRI signal.
