Regularized super-resolution for diffusion MRI

Author

Nedjati-Gilani, Shahrum ; Alexander, Daniel C. ; Parker, Geoff J M

Author_Institution

Centre for Med. Image Comput., Univ. Coll. London, London

fYear

2008

fDate

14-17 May 2008

Firstpage

875

Lastpage

878

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.

Keywords

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; Anisotropic magnetoresistance; Biomedical imaging; Biomedical measurements; Diffusion tensor imaging; Image resolution; Inverse problems; Magnetic resonance imaging; Optimization methods; Spatial resolution; Tensile stress; Diffusion; MRI; regularization; super-resolution;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on

Conference_Location

Paris

Print_ISBN

978-1-4244-2002-5

Electronic_ISBN

978-1-4244-2003-2

Type

conf

DOI

10.1109/ISBI.2008.4541136

Filename

4541136