A variational model for denoising high angular resolution diffusion imaging

Author

Tong, M. ; Kim, Y. ; Zhan, L. ; Sapiro, G. ; Lenglet, C. ; Mueller, B.A. ; Thompson, P.M. ; Vese, L.A.

Author_Institution

Dept. of Math., Univ. of California, Los Angeles, CA, USA

fYear

2012

fDate

2-5 May 2012

Firstpage

530

Lastpage

533

Abstract

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.

Keywords

biodiffusion; biomedical MRI; brain; noise; numerical analysis; phantoms; variational techniques; 2D synthetic data; 3D diffusion-weighted magnetic resonance imaging; 3D real HARDI brain data; DW-MRI; HARDI denoising; Rician noise; fiber pathways; fractional anisotropy; hardware phantom; high angular resolution diffusion imaging; numerical analysis; orientation distribution functions; synthetic fibers; variational model; Data models; Magnetic resonance imaging; Noise; Noise measurement; Noise reduction; Numerical models;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Barcelona

ISSN

1945-7928

Print_ISBN

978-1-4577-1857-1

Type

conf

DOI

10.1109/ISBI.2012.6235602

Filename

6235602