Spherical Harmonic Residual Network for Diffusion Signal Harmonization

Koppers S, Bloy L, Berman J, Tax CMW, Edgar JC, Merhof D (2019)

Publication Type: Conference contribution

Publication year: 2019

Publisher: Springer Heidelberg

Pages Range: 173-182

Conference Proceedings Title: Mathematics and Visualization

Event location: Granada, ESP

ISBN: 9783030058302

DOI: 10.1007/978-3-030-05831-9_14

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.

Involved external institutions

Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE) Children's Hospital of Philadelphia US United States (USA) (US) Cardiff University GB United Kingdom (GB)

How to cite

APA:

Koppers, S., Bloy, L., Berman, J., Tax, C.M.W., Edgar, J.C., & Merhof, D. (2019). Spherical Harmonic Residual Network for Diffusion Signal Harmonization. In Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M.W. Tax (Eds.), Mathematics and Visualization (pp. 173-182). Granada, ESP: Springer Heidelberg.

MLA:

Koppers, Simon, et al. "Spherical Harmonic Residual Network for Diffusion Signal Harmonization." Proceedings of the International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M.W. Tax, Springer Heidelberg, 2019. 173-182.

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