Paper Info
Title
Probabilistic ODF estimation from reduced HARDI data with sparse regularization.
Abstract
High Angular Resolution Diffusion Imaging (HARDI) demands a higher amount of data measurements compared to Diffusion Tensor Imaging (DTI), restricting its use in practice. We propose to represent the probabilistic Orientation Distribution Function (ODF) in the frame of Spherical Wavelets (SW), where it is highly sparse. From a reduced subset of measurements (nearly four times less than the standard for HARDI), we pose the estimation as an inverse problem with sparsity regularization. This allows the fast computation of a positive, unit-mass, probabilistic ODF from 14-16 samples, as we show with both synthetic diffusion signals and real HARDI data with typical parameters.
Year
DOI
Venue
2011
10.1007/978-3-642-23629-7_23
MICCAI (2)
Keywords
Field
DocType
probabilistic orientation distribution,fast computation,real hardi data,data measurement,high angular resolution diffusion,probabilistic odf,probabilistic odf estimation,reduced hardi data,diffusion tensor imaging,sparse regularization,inverse problem,spherical wavelets,higher amount
Computer vision,Diffusion MRI,Pattern recognition,Computer science,Sparse approximation,Spherical harmonics,Regularization (mathematics),Artificial intelligence,Inverse problem,Probabilistic logic,Compressed sensing,Wavelet
Conference
Volume
Issue
ISSN
14
Pt 2
0302-9743
Citations 
PageRank 
References 
13
0.87
5
Authors
2
Name
Order
Citations
PageRank
Antonio Tristan-Vega118716.88
Carl-fredrik Westin22040173.83