ITK Working Group DTI

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Revision as of 22:17, 24 April 2009 by Lbloy (talk | contribs)
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This page is intended to list proposals for changes to ITK to improve the processing of diffusion weighted and diffusion tensor images.


  1. neither itkDiffusionTensor3D or itkSymmetricSecondRankTensor have full numericTraits. DiffusionTensor3D doesn't have any.
  2. static_cast< DT > doesn't work. incorporating constructors like
template < typename TCoordRepB >
SymmetricSecondRankTensor( const SymmetricSecondRankTensor<TCoordRepB,NDimension> & pa ): BaseArray(pa) { };

would allow this type of casting

  1. the pre/post multiply methods in itkSymmetricSecondRankTensor have the wrong api bug0008574

the product of a symmetric matrix and a matrix is not symmetric. A possible solution may be this. add to itkSymmetricSecondRankTensor

Self PreMultiply( const Self & m ) const;

this is ok because a sym matrix times a sym matrix is still symmetric. modify

Self PreMultiply( const MatrixType & m ) const;

to check if m is symmetric, if it is then call Premultiply( static_cast<Self> m) if m isn't throw an exception. Granted this might break existing code, however it will only break code that was giving incorrect results. I think that is preferable to maintaining the status quo.

Feature Requests

  1. A rotate method should be implemented bug0008574 in itkSymmetricSecondRankTensor. I propose the following signatures to enable some generality.
 template<typename TMatrixValueType>
 Self Rotate( const Matrix<TMatrixValueType, NDimension, NDimension> & m);
 template<typename TMatrixValueType>
 Self Rotate( const vnl_matrix_fixed<TMatrixValueType, NDimension, NDimension> & m)
   return this->Rotate( static_cast<Matrix<TMatrixValueType, NDimension, NDimension> >(m) );
 template<typename TMatrixValueType>
 Self Rotate( const vnl_matrix<TMatrixValueType> & m)
   return this->Rotate( static_cast<Matrix<TMatrixValueType> >(m) );
  1. adds support for the log-euclidean metric for dti.
    1. add GetMatrixExp( ) to SymmetricSecondRankTensor which computes the matrix exponential and returns a SymmetricSecondRankTensor
    2. add GetMatrixLog() to DiffusionTensor3D, which computes the matrix log and returns a SymmetricSecondRankTensor
    3. add a naryMeanImagefilter that uses the log-euc metric to compute the means.
  2. Add tests for everything.

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