Probabilistic Monte Carlo based mapping of cerebral connections utilising whole-brain crossing fibre information.
In: Taylor, C and Noble, JA, (eds.)
INFORMATION PROCESSING IN MEDICAL IMAGING, PROCEEDINGS.
(pp. 684 - 695).
A methodology is presented for estimation of a probability density function of cerebral fibre orientations when one or two fibres are present in a voxel. All data are acquired on a clinical MR scanner, using widely available acquisition techniques. The method models measurements of water diffusion in a single fibre by a Gaussian density function and in multiple fibres by a mixture of Gaussian densities. The effects of noise on complex MR diffusion weighted data are explicitly simluated and parameterised. This information is used for standard and Monte Carlo streamline methods. Deterministic and probabilistic maps of anatomical voxel scale connectivity between brain regions are generated.
|Title:||Probabilistic Monte Carlo based mapping of cerebral connections utilising whole-brain crossing fibre information|
|Event:||18th International Conference on Information Processing in Medical Imaging|
|Location:||ST MARTINS COLL, AMBLESIDE, ENGLAND|
|Dates:||2003-07-20 - 2003-07-25|
|Keywords:||DIFFUSION-TENSOR MRI, ANISOTROPY|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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