@inproceedings{93e1f7eecac2438993a75e31a48dceb0,
title = "A variational model for denoising high angular resolution diffusion imaging",
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.",
author = "M. Tong and Y. Kim and L. Zhan and G. Sapiro and C. Lenglet and Mueller, {B. A.} and Thompson, {P. M.} and Vese, {L. A.}",
year = "2012",
month = aug,
day = "15",
doi = "10.1109/ISBI.2012.6235602",
language = "English (US)",
isbn = "9781457718588",
series = "Proceedings - International Symposium on Biomedical Imaging",
pages = "530--533",
booktitle = "2012 9th IEEE International Symposium on Biomedical Imaging",
note = "2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 ; Conference date: 02-05-2012 Through 05-05-2012",
}