## Abstract

High angular resolution diffusion imaging (HARDI) has become an important magnetic resonance technique for in vivo imaging. Current techniques for estimating the diffusion orientation distribution function (ODF), i.e., the probability density function of water diffusion along any direction, do not enforce the estimated ODF to be nonnegative or to sum up to one. Very often this leads to an estimated ODF which is not a proper probability density function. In addition, current methods do not enforce any spatial regularity of the data. In this paper, we propose an estimation method that naturally constrains the estimated ODF to be a proper probability density function and regularizes this estimate using spatial information. By making use of the spherical harmonic representation, we pose the ODF estimation problem as a convex optimization problem and propose a coordinate descent method that converges to the minimizer of the proposed cost function. We illustrate our approach with experiments on synthetic and real data.

Original language | English (US) |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Pages | 877-885 |

Number of pages | 9 |

Volume | 5761 LNCS |

Edition | PART 1 |

DOIs | |

State | Published - 2009 |

Event | 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009 - London, United Kingdom Duration: Sep 20 2009 → Sep 24 2009 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Number | PART 1 |

Volume | 5761 LNCS |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009 |
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Country/Territory | United Kingdom |

City | London |

Period | 9/20/09 → 9/24/09 |

### Bibliographical note

Funding Information:Work supported by startup funds from JHU, by grants NSF CAREER IIS-0447739, NIH R01 HD050735, NIH R01 EB007813, NIH P41 RR008079, NIH P30 NS057091, ONR N00014-05-10836 and ONR N00014-09-1-0084.