Abstract #3795

Iterative model-based Rician bias correction and its application to denoising in diffusion MRI

J-Donald Tournier^1,2, Ben Jeurissen^3,4, and Daan Christiaens^5,6

¹Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ²Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ³imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium, ⁴Lab for Equilibrium Investigations and Aerospace, Department of Physics, University of Antwerp, Antwerp, Belgium, ⁵Medical Imaging Research Center, KU Leuven, Leuven, Belgium, ⁶Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium

Synopsis

Keywords: Data Analysis, Diffusion/other diffusion imaging techniquesRician bias is a long-standing problem in diffusion MRI. We propose a simple iterative strategy to mitigate the problem when a voxel-wise model of the signal is available, and demonstrate its efficacy when fitting spherical harmonics and when included in a denoising strategy.

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