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Open AccessJournal ArticleDOI

Q‐ball imaging

David S. Tuch
- 01 Dec 2004 - 
- Vol. 52, Iss: 6, pp 1358-1372
TLDR
This work has shown that it is possible to resolve intravoxel fiber crossing using mixture model decomposition of the high angular resolution diffusion imaging (HARDI) signal, but mixture modeling requires a model of the underlying diffusion process.
Abstract
Magnetic resonance diffusion tensor imaging (DTI) provides a powerful tool for mapping neural histoarchitecture in vivo. However, DTI can only resolve a single fiber orientation within each imaging voxel due to the constraints of the tensor model. For example, DTI cannot resolve fibers crossing, bending, or twisting within an individual voxel. Intravoxel fiber crossing can be resolved using q-space diffusion imaging, but q-space imaging requires large pulsed field gradients and time-intensive sampling. It is also possible to resolve intravoxel fiber crossing using mixture model decomposition of the high angular resolution diffusion imaging (HARDI) signal, but mixture modeling requires a model of the underlying diffusion process. Recently, it has been shown that the HARDI signal can be reconstructed model-independently using a spherical tomographic inversion called the Funk–Radon transform, also known as the spherical Radon transform. The resulting imaging method, termed q-ball imaging, can resolve multiple intravoxel fiber orientations and does not require any assumptions on the diffusion process such as Gaussianity or multi-Gaussianity. The present paper reviews the theory of q-ball imaging and describes a simple linear matrix formulation for the q-ball reconstruction based on spherical radial basis function interpolation. Open aspects of the q-ball reconstruction algorithm are discussed. Magn Reson Med 52:1358–1372, 2004. © 2004 Wiley-Liss, Inc.

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Citations
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Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?

TL;DR: It is shown that multi-fibre tractography offers significant advantages in sensitivity when tracking non-dominant fibre populations, but does not dramatically change tractography results for the dominant pathways.
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White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI

TL;DR: The physics of DW-MRI is reviewed, currently preferred methodology is indicated, and the limits of interpretation of its results are explained, with a list of 'Do's and Don'ts' which define good practice in this expanding area of imaging neuroscience.
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Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.

TL;DR: The introduction of a constraint on such negative regions is proposed to improve the conditioning of the spherical deconvolution, and this approach is shown to provide FOD estimates that are robust to noise whilst preserving angular resolution.
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Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution.

TL;DR: This study proposes a novel method for estimating the fiber orientation distribution directly from high angular resolution diffusion-weighted MR data without the need for prior assumptions regarding the number of fiber populations present, assuming that all white matter fiber bundles in the brain share identical diffusion characteristics.
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Diffusion Tensor Imaging (DTI)-based White Matter Mapping in Brain Research: A Review

TL;DR: While DTI offers a powerful tool to study and visualize white matter, it suffers from inherent artifacts and limitations and provides an efficient tool for comprehensive, noninvasive, functional anatomy mapping of the human brain.
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