Auto-Calibrating Spherical Deconvolution Based on ODF Sparsity

Thomas Schultz and Samuel Gröschel
In proceedings of Medical Image Computing and Computer-Assisted Intervention, Part I, pages 663-670, Springer, 2013
 

Abstract

Spherical deconvolution infers fiber distributions from diffusion MRI by modeling the signal as the convolution of a fiber orientation density function (fODF) with a single fiber response. We propose a novel calibration procedure that automatically determines this fiber response. This has three advantages: First, the user no longer needs to provide an estimate of the fiber response. Second, we estimate a per-voxel fiber response, which is more adequate for the analysis of patient data with focal white matter degeneration. Third, parameters of the estimated response reflect diffusion properties of the white matter tissue, and can be used for quantitative analysis.

Our method works by finding a tradeoff between a low fitting error and a sparse fODF. Results on simulated data demonstrate that auto-calibration successfully avoids erroneous fODF peaks that can occur with standard deconvolution, and that it resolves fiber crossings with better angular resolution than FORECAST, an alternative method. Parameter maps and tractography results corroborate applicability to clinical data.

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Bibtex

@INPROCEEDINGS{SchultzMICCAI2013,
     author = {Schultz, Thomas and Gr{\"o}schel, Samuel},
      pages = {663--670},
      title = {Auto-Calibrating Spherical Deconvolution Based on ODF Sparsity},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention, Part I},
     series = {LNCS},
     volume = {8149},
       year = {2013},
  publisher = {Springer},
   abstract = {Spherical deconvolution infers fiber distributions from diffusion MRI by modeling the signal as the
               convolution of a fiber orientation density function (fODF) with a single fiber response. We propose
               a novel calibration procedure that automatically determines this fiber response. This has three
               advantages: First, the user no longer needs to provide an estimate of the fiber response. Second, we
               estimate a per-voxel fiber response, which is more adequate for the analysis of patient data with
               focal white matter degeneration. Third, parameters of the estimated response reflect diffusion
               properties of the white matter tissue, and can be used for quantitative analysis.
               
               Our method works by finding a tradeoff between a low fitting error and a sparse fODF. Results on
               simulated data demonstrate that auto-calibration successfully avoids erroneous fODF peaks that can
               occur with standard deconvolution, and that it resolves fiber crossings with better angular
               resolution than FORECAST, an alternative method. Parameter maps and tractography results corroborate
               applicability to clinical data.}
}