Spatially regularized q-ball imaging using spherical ridgelets

In this note, a novel approach to the problem of estimation of orientation distribution functions (ODFs) in q-ball imaging is presented. Rather than recovering the ODFs in a sequential manner, the proposed method performs a concurrent estimation of a set of ODFs pertaining to a specified volume. In...

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Bibliographic Details
Main Authors: Michailovich, Oleg V, Rathi, Yogesh
Format: Conference Proceeding
Language:English
Subjects:
Distributed computing
Distribution functions
High-resolution imaging
Image reconstruction
Image resolution
Magnetic resonance imaging
Noise measurement
Noise reduction
Noise robustness
Spatial resolution
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Summary:In this note, a novel approach to the problem of estimation of orientation distribution functions (ODFs) in q-ball imaging is presented. Rather than recovering the ODFs in a sequential manner, the proposed method performs a concurrent estimation of a set of ODFs pertaining to a specified volume. In this way, the method takes into account the spatial dependencies between different ODFs which are related to the same neural fiber tracts. Such spatial regularization is proven to be a useful tool to use for low SNR data, in which case it allows substantially improving the directional resolution of q-ball imaging. Finally, the reconstruction is based on representing the ODFs using the multi-resolutional basis of spherical ridgelets, which offers a number of additional advantages such as a significant reduction in data dimensionality and remarkable robustness to noises.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2010.5490205