Abstract
The development of Diffusion Tensor MRI has raised hopes in the neuro-science community for in vivo methods to track fiber paths in the white matter. A number of approaches have been presented, but there are still several essential problems that need to be solved. In this paper a novel fiber propagation model is proposed, based on stochastics and regularization, allowing paths originating in one point to branch and return a probability distribution of possible paths. The proposed method utilizes the principles of a statistical Monte Carlo method called Sequential Importance Sampling and Resampling (SISR).
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Keywords
- Importance Sampling
- Diffusion Tensor Magnetic Resonance Image
- Stochastic Part
- Spherical Tensor
- Rough Model
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Björnemo, M., Brun, A., Kikinis, R., Westin, CF. (2002). Regularized Stochastic White Matter Tractography Using Diffusion Tensor MRI. In: Dohi, T., Kikinis, R. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45786-0_54
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DOI: https://doi.org/10.1007/3-540-45786-0_54
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