A VARIATIONAL MODEL FOR DENOISING HIGH ANGULAR RESOLUTION DIFFUSION IMAGING.

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.

Full Text

Duke Authors

Cited Authors

  • Tong, M; Kim, Y; Zhan, L; Sapiro, G; Lenglet, C; Mueller, BA; Thompson, PM; Vese, LA

Published Date

  • 2012

Published In

Start / End Page

  • 530 - 533

PubMed ID

  • 22902985

International Standard Serial Number (ISSN)

  • 1945-7928

Digital Object Identifier (DOI)

  • 10.1109/ISBI.2012.6235602

Language

  • ENG