Multiple Q-Shell ODF Reconstruction in Q-Ball Imaging

Authors:
Iman Aganj;Christophe Lenglet;Guillermo Sapiro;Essa Yacoub;Kamil Ugurbil;Noam Harel
Affiliations:
Department of Electrical and Computer Engineering, University of Minnesota, USA;Department of Electrical and Computer Engineering, University of Minnesota, USA and Center for Magnetic Resonance Research, University of Minnesota, USA;Department of Electrical and Computer Engineering, University of Minnesota, USA;Center for Magnetic Resonance Research, University of Minnesota, USA;Center for Magnetic Resonance Research, University of Minnesota, USA;Center for Magnetic Resonance Research, University of Minnesota, USA
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

Citing 2
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ODF reconstruction in Q-ball imaging with solid angle consideration

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Estimating Orientation Distribution Functions with Probability Density Constraints and Spatial Regularity

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I

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Abstract

Q-ball imaging (QBI) is a high angular resolution diffusion imaging (HARDI) technique which has been proven very successful in resolving multiple intravoxel fiber orientations in MR images. The standard computation of the orientation distribution function (ODF, the probability of diffusion in a given direction) from q-ball uses linear radial projection, neglecting the change in the volume element along the ray, thereby resulting in distributions different from the true ODFs. A new technique has been recently proposed that, by considering the solid angle factor, uses the mathematically correct definition of the ODF and results in a dimensionless and normalized ODF expression from a single q-shell. In this paper, we extend this technique in order to exploit HARDI data from multiple q-shells. We consider the more flexible multi-exponential model for the diffusion signal, and show how to efficiently compute the ODFs in constant solid angle. We describe our method and demonstrate its improved performance on both artificial and real HARDI data.