Generalized q-Sampling Imaging

@article{Yeh2010GeneralizedQI,
  title={Generalized q-Sampling Imaging},
  author={Fang-Cheng Yeh and Van J. Wedeen and Wen-Yih Isaac Tseng},
  journal={IEEE transactions on medical imaging},
  year={2010},
  volume={29 9},
  pages={
          1626-35
        }
}
Based on the Fourier transform relation between diffusion magnetic resonance (MR) signals and the underlying diffusion displacement, a new relation is derived to estimate the spin distribution function (SDF) directly from diffusion MR signals. This relation leads to an imaging method called generalized q-sampling imaging (GQI), which can obtain the SDF from the shell sampling scheme used in q-ball imaging (QBI) or the grid sampling scheme used in diffusion spectrum imaging (DSI). The accuracy… 

Figures and Tables from this paper

Reconstruction of major fibers using 7T multi-shell Hybrid Diffusion Imaging in mice
TLDR
It is found that QBI may provide greater reconstruction accuracy for major fibers, which improves with the addition of higher b-value shells, unlike GQI or DTI (as expected).
7T multi-shell hybrid diffusion imaging (HYDI) for mapping brain connectivity in mice
TLDR
High-resolution connectivity mapping with 7T HYDI offers great potential for understanding unresolved changes in mouse models of brain disease.
NTU-90: A high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction
Effect of Data Acquisition and Analysis Method on Fiber Orientation Estimation in Diffusion MRI
TLDR
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.
Accelerated diffusion spectrum imaging via compressed sensing for the human connectome project
TLDR
The proposed compressed sensing based diffusion spectrum imaging (CS-DSI) method is validated against a ground truth from synthetic data mimicking the FiberCup phantom, demonstrating the robustness of CS- DSI on accurately estimating underlying fiber orientations from noisy diffusion data.
A Comparative Study of Diffusion Fiber Reconstruction Models for Pyramidal Tract Branches
TLDR
GQI-based group probabilistic maps of PT branches showed that the four PT branches exhibited relatively unique spatial distributions, and the GQI and QBI represent better diffusion models for the PT and PT branches.
Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI
TLDR
Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest.
Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging
TLDR
The analysis result showed that the diffusion signals, anisotropy, diffusivity, and connectivity matrix of the HARDI converted from multi-shell and DSI were highly correlated with those of theHARDI acquired on the MR scanner, with correlation coefficients around 0.8~0.9.
...
...

References

SHOWING 1-10 OF 41 REFERENCES
Practical crossing fiber imaging with combined DTI datasets and generalized reconstruction algorithm
We present a clinically feasible imaging scheme that combines two DTI datasets to obtain a high angular resolution reconstruction of crossing fibers. The scheme was designed based on a paradigm that
Q‐ball reconstruction of multimodal fiber orientations using the spherical harmonic basis
TLDR
Reconstruction of the q‐ball orientation distribution function (ODF) is reformulated in terms of spherical harmonic basis functions, yielding an analytic solution with useful properties of a frequency domain representation that brings the technique closer to clinical feasibility from the standpoint of total imaging time.
Hybrid diffusion imaging
Measurement of fiber orientation distributions using high angular resolution diffusion imaging
TLDR
The new method addresses the problem of partial volume averaging in diffusion tensor imaging and provides a basis for more reliable estimates of fiber orientation and fractional anisotropy.
Diffusion tensor MR imaging of the human brain.
TLDR
A quantitative characterization of water diffusion in anisotropic, heterogeneously oriented tissues is clinically feasible and should improve the neuroradiologic assessment of a variety of gray and white matter disorders.
Biexponential and diffusional kurtosis imaging, and generalised diffusion-tensor imaging (GDTI) with rank-4 tensors: a study in a group of healthy subjects
TLDR
Normative data for biexponential, diffusional and diffusional kurtosis models are provided, which can be used for reference in future studies and in clinical settings.
Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging
TLDR
From the study of six healthy adult subjects, the excess diffusional kurtosis is found to be significantly higher in white matter than in gray matter, reflecting the structural differences between these two types of cerebral tissues.
Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging
TLDR
Methods are presented to map complex fiber architectures in tissues by imaging the 3D spectra of tissue water diffusion with MR, showing correspondence between the orientational maxima of the diffusion spectrum and those of the fiber orientation density at each location.
...
...