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http://cimat.repositorioinstitucional.mx/jspui/handle/1008/908| Diffusion Basis Functions Decomposition for Estimating White Matter Intravoxel Fiber Geometry | |
| MARIANO JOSE JUAN RIVERA MERAZ | |
| Acceso Abierto | |
| Atribución-NoComercial-CompartirIgual | |
| Resonancia Magnética | |
| In this paper, we present a new formulation for recovering the fiber tract geometry within a voxel from diffusion weighted magnetic resonance imaging (MRI) data, in the presence of single or multiple neuronal fibers. To this end, we define a discrete set of diffusion basis functions. The intravoxel information is recovered at voxels containing fiber crossings or bifurcations via the use of a linear combination of the above mentioned basis functions. Then, the parametric representation of the intravoxel fiber geometry is a discrete mixture of Gaussians. Our synthetic experiments depict several advantages by using this discrete schema: the approach uses a small number of diffusion weighted images (23) and relatively small values (1250 s mm 2), i.e., the intravoxel information can be inferred at a fraction of the acquisition time required for datasets involving a large number of diffusion gradient orientations. Moreover our method is robust in the presence of more than two fibers within a voxel, improving the state-of-the-art of such parametric models. We present two algorithmic solutions to our formulation: by solving a linear program or by minimizing a quadratic cost function (both with non-negativity constraints). Such minimizations are efficiently achieved with standard iterative deterministic algorithms. Finally, we present results of applying the algorithms to synthetic as well as real data. | |
| IEEE | |
| 2007 | |
| Artículo | |
| Inglés | |
| Investigadores | |
| ORDENADORES DIGITALES | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Ciencias de la Computación |
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| MRivera1.pdf | 1.32 MB | Adobe PDF | Visualizar/Abrir |