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Fast and Accurate Reconstruction of HARDI Data Using Compressed Sensing

  • Oleg Michailovich
  • Yogesh Rathi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6361)

Abstract

A spectrum of brain-related disorders are nowadays known to manifest themselves in degradation of the integrity and connectivity of neural tracts in the white matter of the brain. Such damage tends to affect the pattern of water diffusion in the white matter – the information which can be quantified by diffusion MRI (dMRI). Unfortunately, practical implementation of dMRI still poses a number of challenges which hamper its wide-spread integration into regular clinical practice. Chief among these is the problem of long scanning times. In particular, in the case of High Angular Resolution Diffusion Imaging (HARDI), the scanning times are known to increase linearly with the number of diffusion-encoding gradients. In this research, we use the theory of compressive sampling (aka compressed sensing) to substantially reduce the number of diffusion gradients without compromising the informational content of HARDI signals. The experimental part of our study compares the proposed method with a number of alternative approaches, and shows that the former results in more accurate estimation of HARDI data in terms of the mean squared error.

Keywords

Sparse Representation Compressed Sensing Convex Optimization Problem High Angular Resolution Discrete Orientation 
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 2010

Authors and Affiliations

  • Oleg Michailovich
    • 1
  • Yogesh Rathi
    • 2
  1. 1.Department of ECEUniversity of Waterloo 
  2. 2.Brigham and Women’s HospitalHarvard Medical School 

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