Abstract
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 learningresidual 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.
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Acknowledgements
The authors would like to thank the 2017 computational dMRI challenge organizers (Francesco Grussu, Enrico Kaden, Lipeng Ning and Jelle Veraart) for help with data acquisition and processing, as well as Derek Jones, Umesh Rudrapatna, John Evans, Slawomir Kusmia, Cyril Charron, and David Linden at CUBRIC, Cardiff University, and Fabrizio Fasano at Siemens for their support with data acquisition.
This work was supported by a Rubicon grant from the NWO (680-50-1527), a Wellcome Trust Investigator Award (096646/Z/11/Z), and a Wellcome Trust Strategic Award (104943/Z/14/Z). The data were acquired at the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure funded by the EPSRC (grant EP/M029778/1), and The Wolfson Foundation. This work was supported by the International Research Training Group 2150 of the German Research Foundation (DFG).
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Koppers, S., Bloy, L., Berman, J.I., Tax, C.M.W., Edgar, J.C., Merhof, D. (2019). Spherical Harmonic Residual Network for Diffusion Signal Harmonization. In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, C. (eds) Computational Diffusion MRI. MICCAI 2019. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-05831-9_14
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