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A variational model for denoising high angular resolution diffusion imaging

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8 Author(s)
Tong, M. ; Dept. of Math., Univ. of California, Los Angeles, CA, USA ; Kim, Y. ; Zhan, L. ; Sapiro, G.
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The presence of noise in High Angular Resolution Diffusion Imaging (HARDI) data of the brain can limit the accuracy with which fiber pathways of the brain can be extracted. In this work, we present a variational model to denoise HARDI data corrupted by Rician noise. Numerical experiments are performed on three types of data: 2D synthetic data, 3D diffusion-weighted Magnetic Resonance Imaging (DW-MRI) data of a hardware phantom containing synthetic fibers, and 3D real HARDI brain data. Experiments show that our model is effective for denoising HARDI-type data while preserving important aspects of the fiber pathways such as fractional anisotropy and the orientation distribution functions.

Published in:

Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on

Date of Conference:

2-5 May 2012

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