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Probabilistic ODF Estimation from Reduced HARDI Data with Sparse Regularization

  • Antonio Tristán-Vega
  • Carl-Fredrik Westin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

High Angular Resolution Diffusion Imaging (HARDI) demands a higher amount of data measurements compared to Diffusion Tensor Imaging (DTI), restricting its use in practice. We propose to represent the probabilistic Orientation Distribution Function (ODF) in the frame of Spherical Wavelets (SW), where it is highly sparse. From a reduced subset of measurements (nearly four times less than the standard for HARDI), we pose the estimation as an inverse problem with sparsity regularization. This allows the fast computation of a positive, unit-mass, probabilistic ODF from 14-16 samples, as we show with both synthetic diffusion signals and real HARDI data with typical parameters.

Keywords

Diffusion Tensor Image Spherical Harmonics Sparse Representation Compressed Sensing Orientation Distribution Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antonio Tristán-Vega
    • 1
  • Carl-Fredrik Westin
    • 1
  1. 1.Laboratory of Mathematics in ImagingBrigham and Women’s HospitalBostonUSA

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