Information Processing in Medical Imaging

Volume 2732 of the series Lecture Notes in Computer Science pp 684-695

Probabilistic Monte Carlo Based Mapping of Cerebral Connections Utilising Whole-Brain Crossing Fibre Information

  • Geoff J. M. ParkerAffiliated withImaging Science & Biomedical Engineering, University of Manchester
  • , Daniel C. AlexanderAffiliated withDepartment of Computer Science, University College London

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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.