Reconstruction of the orientation distribution function in single‐ and multiple‐shell q‐ball imaging within constant solid angle

@article{Aganj2010ReconstructionOT,
  title={Reconstruction of the orientation distribution function in single‐ and multiple‐shell q‐ball imaging within constant solid angle},
  author={Iman Aganj and Christophe Lenglet and Guillermo Sapiro and Essa Yacoub and K{\^a}mil Uğurbil and Noam Y. Harel},
  journal={Magnetic Resonance in Medicine},
  year={2010},
  volume={64}
}
q‐Ball imaging is a high‐angular‐resolution diffusion imaging technique that has been proven very successful in resolving multiple intravoxel fiber orientations in MR images. The standard computation of the orientation distribution function (the probability of diffusion in a given direction) from q‐ball data uses linear radial projection, neglecting the change in the volume element along each direction. This results in spherical distributions that are different from the true orientation… 
Model-free marginal orientation distribution function reconstruction in single-shell Q-Ball imaging
  • N. Zhang
  • Computer Science
    2011 4th International Conference on Biomedical Engineering and Informatics (BMEI)
  • 2011
TLDR
A novel model-free and single-shell HARDI method for analytical reconstruction of the marginal ODF based on Funk-Radon transform (FRT) is proposed and its complexity is comparable to that of the original QBI.
ODF reconstruction in q-ball imaging with solid angle consideration
TLDR
This paper considers the mathematically correct definition of the ODF and derive a closed-form expression for it in QBI and derives a derived ODF that is dimensionless and normalized, and can be efficiently computed from q-ball acquisition protocols.
Estimation of fiber orientation and spin density distribution by diffusion deconvolution
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.
Reconstruction and description of the orientation distribution function of high angular resolution diffusion imaging. (Reconstruction et description des fonctions de distribution d'orientation en imagerie de diffusion à haute résolution angulaire)
TLDR
A new metric, called PEAM (PEAnut Metric), which is based on computing the deviation of ODFs from a single fiber ODF represented by a peanut was proposed and used to classify intravoxel fiber configurations and showed that the characteristics of 3D point clouds can be well assessed in a relatively complete and quantitative manner.
Generalized Constant Solid Angle ODF and Optimal Acquisition Protocol for Fiber Orientation Mapping
TLDR
The generalized CSA-ODF model performs optimally with 200 q-space data points distributed over three shells acquired at b = 1000, 2000s/mm and in the range [3000, 6000]s/ mm for the third shell.
Fiber ball imaging
Leading non‐Gaussian corrections for diffusion orientation distribution function
TLDR
Results indicate that the inclusion of the leading non‐Gaussian corrections can significantly affect fiber tractography in white matter regions, such as the centrum semiovale, where fiber crossings are common.
Online orientation distribution function reconstruction in constant solid angle and its application to motion detection in HARDI
TLDR
This work adapts real-time algorithms to the mathematically correct definition of ODF in constant solid angle (CSA), and develops a motion detection algorithm upon this reconstruction.
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TLDR
This paper considers the more flexible multi-exponential model for the diffusion signal, and shows how to efficiently compute the ODFs in constant solid angle on both artificial and real HARDI data.
ODF reconstruction in q-ball imaging with solid angle consideration
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This paper considers the mathematically correct definition of the ODF and derive a closed-form expression for it in QBI and derives a derived ODF that is dimensionless and normalized, and can be efficiently computed from q-ball acquisition protocols.
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TLDR
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