Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI

@article{Cheng2014NonNegativeSD,
  title={Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI},
  author={Jian Cheng and Rachid Deriche and Tianzi Jiang and Dinggang Shen and Pew-Thian Yap},
  journal={NeuroImage},
  year={2014},
  volume={101},
  pages={750-764}
}
Spherical Deconvolution (SD) is commonly used for estimating fiber Orientation Distribution Functions (fODFs) from diffusion-weighted signals. Existing SD methods can be classified into two categories: 1) Continuous Representation based SD (CR-SD), where typically Spherical Harmonic (SH) representation is used for convenient analytical solutions, and 2) Discrete Representation based SD (DR-SD), where the signal profile is represented by a discrete set of basis functions uniformly oriented on… Expand
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References

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Non-negative Spherical Deconvolution (NNSD) for Fiber Orientation Distribution Function Estimation
TLDR
NNSD is the first SH based method that guarantees non-negativity of the fODF throughout the unit sphere, and unlike approaches such as Maximum Entropy SD, Cartesian Tensor Fiber Orientation Distribution, and discrete representation based SD (DR-SD), NNSD yields improved performance for both synthetic and real data. Expand
Non-Local Non-Negative Spherical Deconvolution for Single and Multiple Shell Diffusion MRI
In diffusion MRI (dMRI), Spherical Deconvolution (SD) is a category of methods which estimate the fiber Orientation Distribution Function (fODF). Existing SD methods, including the widely usedExpand
Model-Free, Regularized, Fast, and Robust Analytical Orientation Distribution Function Estimation
TLDR
A uniform analytical method is proposed to estimate these two ODFs from DWI signals in q space, which is based on Spherical Polar Fourier Expression (SPFE) of signals, which works well in all experiments, especially for the data with low SNR, low anisotropy and non-exponential decay. Expand
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A likelihood function based on the Rician distribution of the NMR signals and a prior distributions considering ODFs as Riemanian manifolds are derived, which allows for effectively reconstruct and regularize ODF images in one step within this Riemannian framework. Expand
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