Paper Info
Title
Regularized super-resolution for diffusion MRI
Abstract
In this paper, we present a new regularized super-resolution method for finding accurate fibre orientations and volume fractions of fibre populations on a sub-voxel scale from a 3D diffusion MRI acquisition in order to distinguish between various fibre configurations such as fanning and bending, and ameliorate partial volume effects. We treat this task as a general inverse problem, which we solve by regularization and optimization, and demonstrate the method on human brain data.
Year
DOI
Venue
2008
10.1109/ISBI.2008.4541136
ISBI
Keywords
Field
DocType
biological tissues,biomedical MRI,brain,inverse problems,medical signal processing,molecular biophysics,3D diffusion MRI acquisition,ameliorate partial volume effect,bending,fanning,fibre orientation,fibre population,human brain data,inverse problem,regularized super-resolution method,sub-voxel scale,volume fraction,Diffusion,MRI,regularization,super-resolution
Diffusion MRI,Pattern recognition,Computer science,Algorithm,Bending,Regularization (mathematics),Artificial intelligence,Inverse problem,Partial volume,Nuclear magnetic resonance,Superresolution
Conference
ISSN
Citations 
PageRank 
1945-7928
9
0.65
References 
Authors
4
3
Name
Order
Citations
PageRank
Shahrum Nedjati-gilani1131.31
Daniel C. Alexander21553144.96
Geoffrey J. M. Parker344439.62