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Poster — Thurs Eve‐28: New brain diffusion analysis method: White matter grey matter dissasociation
Author(s) -
CárdenasBlanco A,
Olariou E,
Cameron I
Publication year - 2008
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2965947
Subject(s) - diffusion , white matter , diffusion mri , diffusion imaging , noise (video) , grey matter , effective diffusion coefficient , white noise , algorithm , computer science , mathematics , physics , statistics , artificial intelligence , medicine , magnetic resonance imaging , image (mathematics) , radiology , thermodynamics
Diffusion MR studies are often used to investigate the physical properties of brain tissues (1, 2). It is known that a full characterisation of the diffusion decay for brain could give valuable information about the structural organisation of cerebral tissue. The significance of the present diffusion decay study lies in the combination of three novel procedures to provide a better characterization of the diffusion decay: i) the acquisition of a large number of b‐values (96 b‐values up to 10,000 s/mm 2 ), ii) the application of a noise correction technique (3) to the acquired data, and iii) the use of a Non Negative Least Squares (NNLS) fitting algorithm to evaluate the diffusion coefficients. The presence of noise in magnitude MR images can affect the calculation of the diffusion parameters (4) and therefore a noise correction technique (3) is applied. The NNLS algorithm is used to fit the corrected data instead of the more commonly used Levenberg‐Marquardt algorithm since the NNLS algorithm does not require the number of components to be specified, nor does it need initial estimates of the fitting parameters as input; thus giving it more versatility as a fitting tool for the diffusion decay. The results indicate that the diffusion decays in grey and white matter have one and two components, respectively. Consequently, the short diffusion component in white matter (Fig. 1.c) can be used as a tool in the disassociation of white and grey matter tissues.