Abstract
In this paper we tackle the problem of regularisation for inverse problems in single shell diffusion weighted image restoration. Our aim is to recover a high-resolution and denoised DWI signal, prior to any model fitting. The main contribution of our method is the combination of two regularization terms, one using the information arising from the spatial domain, hence analysing the single image, while the other uses information coming from the angular domain, thus using the relationships between the values along different directions within a single voxel. We show that our novel regularization method outperforms widely used and recent DWI denoising algorithms. Additionally we demonstrate that the proposed regularisation technique can be successfully applied to the super-resolution reconstruction of high-resolution volume from thick-slice data. Both scenarios are tested on simulated phantom and real DWI data.
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Acknowledgements
This work was supported by the CIBM of the Unil, the Swiss Federal Institute of Technology Lausanne, the University of Geneva, the CHUV, the Hôpitaux Universitaires de Genève, the Leenaards and Jeantet Foundations. This work was also supported by the Swiss National Science Foundation grant SNSF-IZK0Z2_170894.
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Mella, A., Daducci, A., Orlandi, G., Thiran, JP., Deprez, M., Bach Cuadra, M. (2019). A Novel Spatial-Angular Domain Regularisation Approach for Restoration of Diffusion MRI. 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_4
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DOI: https://doi.org/10.1007/978-3-030-05831-9_4
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