Variational denoising of diffusion weighted MRI | Zendy

Tim McGraw | Zendy; Baba C. Vemuri | Zendy; Evren Özarslan | Zendy; Yunmei Chen | Zendy; Thomas H. Mareci | Zendy

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Variational denoising of diffusion weighted MRI

Author(s) -

Tim McGraw,

Baba C. Vemuri,

Evren Özarslan,

Yunmei Chen,

Thomas H. Mareci

Publication year - 2009

Publication title -

inverse problems and imaging

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.755

H-Index - 40

eISSN - 1930-8345

pISSN - 1930-8337

DOI - 10.3934/ipi.2009.3.625

Subject(s) - smoothing , diffusion mri , computation , noise reduction , regularization (linguistics) , computer science , algorithm , norm (philosophy) , quadratic equation , finite element method , synthetic data , mathematics , artificial intelligence , computer vision , physics , geometry , magnetic resonance imaging , radiology , thermodynamics , political science , law , medicine

In this paper, we present a novel variational formulation for restoring high angular resolution diffusion imaging (HARDI) data. The restoration formulation involves smoothing signal measurements over the spherical domain and across the 3D image lattice. The regularization across the lattice is achieved using a total variation (TV) norm based scheme, while the finite element method (FEM) was employed to smooth the data on the sphere at each lattice point using first and second order smoothness constraints. Examples are presented to show the performance of the HARDI data restoration scheme and its effect on fiber direction computation on synthetic data, as well as on real data sets collected from excised rat brain and spinal cord.

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