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- W2185272514 abstract "1 1 e σ t t v x x n n n Δ + Δ + = − − . Here n e represents independent standard normal random vectors, vn is the principal eigenvector of the point xn and t and are step size and noise intensity parameters. If = 0 the algorithm becomes deterministic. Repeating the algorithm 1000 times from a seed point with particular parameters and with the main stopping criterion of FA 0.15 generates two sets of curves (positive and negative directions) from a seed point. We then applied the average curves method to each set of curves and the resultant curves were concatenated to obtain the full fiber path. We used phantom data for estimating for different SNR levels of in vivo data and evaluated the performance of the average curves methods. Phantom Data: The phantom images for simulating tractography errors were generated using the MRtrix software package with the following parameters: number of diffusion encoding directions = 30, b-value = 1000 s/mm 2 , TE = 90 ms, NEX = 4 and resolution:1x1x1 mm 3 . We performed fiber tracking with a constant step size 0.1mm and with different values for different trajectory structures (linear, kissing and spiral) with a range of SNR levels to generate the computed curve C. The true curve T is the axis of each fibre sampled. The performance measures ( ) were calculated using the symmetrised average minimum distance between T and C, and the Hausdorff distance from C to T. In vivo: Diffusion-weighted data were acquired from a healthy human brain on a 1.5 Tesla scanner with an image matrix of 128x128, 60 slice locations covering the" @default.
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- W2185272514 date "2009-01-01" @default.
- W2185272514 modified "2023-09-27" @default.
- W2185272514 title "Stochastic Fibre Tracking: An Average Curves Approach" @default.
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