Article

Q-ball imaging

Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown 02129, USA.
Magnetic Resonance in Medicine (Impact Factor: 3.4). 12/2004; 52(6):1358-72. DOI: 10.1002/mrm.20279
Source: PubMed

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.

3 Followers
 · 
124 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Diffusion Tensor Imaging (DTI) is adversely affected by subject motion. It is necessary to discard the corrupted images before diffusion parameter estimation. However, the consequences of rejecting those images are not well understood. In this study, we investigated the effects of excluding one or more volumes of diffusion weighted images by analyzing the changes in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) and the primary eigenvector (V1). Based on the full set of diffusion images acquired by Jones30 diffusion scheme, we generated incomplete sets of at least six in three different ways: random, uniform and clustered rejections. The results showed that MD was not significantly affected by rejecting diffusion directions. In the cases of random rejections, FA, AD, RD and V1 were overestimated more greatly with increasing number of rejections and the overestimations were worse in low FA regions than high FA regions. For uniform rejections, at which the remaining diffusion directions are evenly distributed on a sphere, little change was observed in FA and in V1. Clustered rejections, on the other hand, displayed the most significant overestimation of the parameters, and the resulting accuracy depended on the relative orientation of the underlying fibers with respect to the excluded directions. In practice, if diffusion direction data is excluded, it is important to note the number and location of directions rejected, in order to make a more precise analysis of the data. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 01/2015; 109. DOI:10.1016/j.neuroimage.2015.01.010 · 6.13 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: BACKGROUND: Progress in neuroimaging has yielded new powerful tools which, potentially, can be applied to clinical populations, improve the diagnosis of neurological disorders and predict outcome. At present, the diagnosis of consciousness disorders is limited to subjective assessment and objective measurements of behavior, with an emerging role for neuroimaging techniques. In this review we focus on white matter alterations measured using Diffusion Tensor Imaging on patients with consciousness disorders, examining the most common diffusion imaging acquisition protocols and considering the main issues related to diffusion imaging analyses. We conclude by considering some of the remaining challenges to overcome, the existing knowledge gaps and the potential role of neuroimaging in understanding the pathogenesis and clinical features of disorders of consciousness.
    Frontiers in Human Neuroscience 01/2015; 8. DOI:10.3389/fnhum.2014.01028 · 2.90 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Linear regression is a parametric model which is ubiquitous in scientific analysis. The classical setup where the observations and responses, i.e., (xi , yi ) pairs, are Euclidean is well studied. The setting where yi is manifold valued is a topic of much interest, motivated by applications in shape analysis, topic modeling, and medical imaging. Recent work gives strategies for max-margin classifiers, principal components analysis, and dictionary learning on certain types of manifolds. For parametric regression specifically, results within the last year provide mechanisms to regress one real-valued parameter, xi ∈ R, against a manifold-valued variable, yi ∈ . We seek to substantially extend the operating range of such methods by deriving schemes for multivariate multiple linear regression -a manifold-valued dependent variable against multiple independent variables, i.e., f : R (n) → . Our variational algorithm efficiently solves for multiple geodesic bases on the manifold concurrently via gradient updates. This allows us to answer questions such as: what is the relationship of the measurement at voxel y to disease when conditioned on age and gender. We show applications to statistical analysis of diffusion weighted images, which give rise to regression tasks on the manifold GL(n)/O(n) for diffusion tensor images (DTI) and the Hilbert unit sphere for orientation distribution functions (ODF) from high angular resolution acquisition. The companion open-source code is available on nitrc.org/projects/riem_mglm.

Preview

Download
2 Downloads
Available from