Abstract
In this paper we investigate the effect of single-shell q-space diffusion sampling strategies and applicable multiple-fiber analysis methods on fiber orientation estimation in Diffusion MRI. Specifically, we develop a simulation based on an in-vivo data set and compare a two-compartment “ball-and-stick” model, a constrained spherical deconvolution approach, a generalized Fourier transform approach, and three related methods based on transforms of Fourier data on the sphere. We evaluate each method for N = 20, 30, 40, 60, 90 and 120 angular diffusion-weighted samples, at SNR = 18 and diffusion-weighting \(b = 1,000\,\mathrm{s}/\mathrm{{mm}}^{2}\), common to clinical studies. Our results quantitatively show the methods are most distinguished from each other by their fiber detection ability. Overall, the “ball-and-stick” model and spherical deconvolution approach were found to perform best, yielding the least orientation error, and greatest detection rate of fibers.
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Wilkins, B., Lee, N., Rajagopalan, V., Law, M., Leporé, N. (2014). Effect of Data Acquisition and Analysis Method on Fiber Orientation Estimation in Diffusion MRI. In: Schultz, T., Nedjati-Gilani, G., Venkataraman, A., O'Donnell, L., Panagiotaki, E. (eds) Computational Diffusion MRI and Brain Connectivity. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-02475-2_2
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DOI: https://doi.org/10.1007/978-3-319-02475-2_2
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