Title :
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
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;
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
DOI :
10.1109/ISBI.2008.4541136
