# 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…

## 357 Citations

Model-free marginal orientation distribution function reconstruction in single-shell Q-Ball imaging

- Computer Science2011 4th International Conference on Biomedical Engineering and Informatics (BMEI)
- 2011

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

- Mathematics2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
- 2009

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

- PhysicsNeuroImage
- 2011

Model-Free, Regularized, Fast, and Robust Analytical Orientation Distribution Function Estimation

- MathematicsMICCAI
- 2010

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)

- Computer Science
- 2014

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

- Computer Science
- 2012

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.

Leading non‐Gaussian corrections for diffusion orientation distribution function

- MathematicsNMR in biomedicine
- 2014

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.

Histological validation of diffusion MRI fiber orientation distributions and dispersion

- PhysicsNeuroImage
- 2018

Online orientation distribution function reconstruction in constant solid angle and its application to motion detection in HARDI

- Physics2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
- 2010

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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