Probabilistic Monte Carlo based mapping of cerebral connections utilising whole-brain crossing fibre information

• Published in: Information processing in medical imaging : proceedings of the ... conference Vol. 18; p. 684
• Main Authors: Parker, Geoff J M, Alexander, Daniel C
• Format: Journal Article
• Language: English
• Published: Germany 01.01.2003
• Subjects: Algorithms; Cerebral Cortex - anatomy & histology; Computer Simulation; Diffusion Magnetic Resonance Imaging - methods; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Models, Biological; Models, Statistical; Monte Carlo Method; Nerve Net - anatomy & histology; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Stochastic Processes
• ISSN: 1011-2499
• DOI: 10.1007/978-3-540-45087-0_57

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.