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This development is supported by a project funded by the German Research Foundation, in which four major obstacles related to dMRI data were addressed: (1) the lack of transferability of dMRI data between clinical sites, (2) the lack of training and label data, (3) the potential of complex diffusion data, and (4) the integration of spherical signals in neural networks to improve accuracy. To overcome the problem of different MRI systems producing slightly varying data, the project developed a method for harmonizing MRI signals. To address the issue of limited ground truth data, a framework was developed to synthesize individual diffusion data and complete datasets based on important diffusion characteristics and statistics. The integration of complex signals, often discarded during acquisition, to improve reconstruction was also explored. Finally, new methods were developed to preserve the spherical character of the diffusion data in the DL model. 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