title: Q-ball imaging.
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.
authors.affiliation: Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown 02129, USA. dtuch@nmr.mgh.harvard.edu
authors.forename: David S
authors.initials: DS
authors.lastname: Tuch
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted