diff --git a/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.cpp b/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.cpp
index 5213be5df8..c318353132 100644
--- a/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.cpp
+++ b/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.cpp
@@ -1,1227 +1,1267 @@
 /*=========================================================================
 
 Program:   Medical Imaging & Interaction Toolkit
 Language:  C++
 Date:      $Date: 2009-07-14 19:11:20 +0200 (Tue, 14 Jul 2009) $
 Version:   $Revision: 18127 $
 
 Copyright (c) German Cancer Research Center, Division of Medical and
 Biological Informatics. All rights reserved.
 See MITKCopyright.txt or http://www.mitk.org/copyright.html for details.
 
 This software is distributed WITHOUT ANY WARRANTY; without even
 the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
 PURPOSE.  See the above copyright notices for more information.
 
 =========================================================================*/
 #ifndef __itkDiffusionMultiShellQballReconstructionImageFilter_cpp
 #define __itkDiffusionMultiShellQballReconstructionImageFilter_cpp
 
 #include <itkDiffusionMultiShellQballReconstructionImageFilter.h>
 #include <itkImageRegionConstIterator.h>
 #include <itkImageRegionConstIteratorWithIndex.h>
 #include <itkImageRegionIterator.h>
 #include <itkArray.h>
 #include <vnl/vnl_vector.h>
 
 #include <boost/version.hpp>
 #include <stdio.h>
 #include <locale>
 #include <strstream>
 
 #define _USE_MATH_DEFINES
 #include <math.h>
 
 #include "mitkSphericalHarmonicsFunctions.h"
 #include "itkPointShell.h"
 #include <memory>
 
 namespace itk {
 
 #define QBALL_ANAL_RECON_PI       M_PI
 
 template< class T, class TG, class TO, int L, int NODF>
 DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::DiffusionMultiShellQballReconstructionImageFilter() :
   m_GradientDirectionContainer(NULL),
   m_NumberOfGradientDirections(0),
   m_NumberOfBaselineImages(1),
   m_Threshold(NumericTraits< ReferencePixelType >::NonpositiveMin()),
   m_BValue(1.0),
   m_Lambda(0.0),
   m_IsHemisphericalArrangementOfGradientDirections(false),
   m_IsArithmeticProgession(false),
   m_ReconstructionType(Standard1Shell)
 {
   // At least 1 inputs is necessary for a vector image.
   // For images added one at a time we need at least six
   this->SetNumberOfRequiredInputs( 1 );
 }
 
 
 template< class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 typename itk::DiffusionMultiShellQballReconstructionImageFilter< TReferenceImagePixelType,TGradientImagePixelType,TOdfPixelType, NOrderL,NrOdfDirections>::OdfPixelType itk::DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType, NOrderL, NrOdfDirections>
 ::Normalize( OdfPixelType odf, typename NumericTraits<ReferencePixelType>::AccumulateType b0 )
 {
   for(int i=0; i<NrOdfDirections; i++)
   {
     odf[i] = odf[i] < 0 ? 0 : odf[i];
     odf[i] *= QBALL_ANAL_RECON_PI*4/NrOdfDirections;
   }
   TOdfPixelType sum = 0;
   for(int i=0; i<NrOdfDirections; i++)
   {
     sum += odf[i];
   }
   if(sum>0)
     odf /= sum;
 
   return odf;
 }
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void itk::DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
-::Threshold(vnl_vector<TOdfPixelType> & vec, float sigma)
+::Threshold(vnl_vector<double> & vec, float sigma)
 {
   for(int i = 0; i < vec.size(); i++)
   {
     if(vec[i] < 0)
     {
       vec[i] = sigma / 2 ;
     }
     if(0 <= vec[i] && vec[i] < sigma )
     {
-      vec[i] = sigma / 2 + (vec[i] * vec[i]) / 2 * sigma;
+      vec[i] = sigma / 2 + (vec[i] * vec[i]) / (2 * sigma);
     }
     if( 1 - sigma <= vec[i] && vec[i] < 1 )
     {
-      vec[i] = 1-(sigma/2) - (( 1 - vec[i] * vec[i] ) / 2 * sigma );
+      vec[i] = 1-(sigma/2) - ((( 1 - vec[i]) * (1 - vec[i] )) / (2 * sigma) );
     }
     if( 1 <= vec[i] )
     {
       vec[i] = 1 - (sigma / 2);
     }
   }
 }
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void itk::DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
-::Threshold(vnl_matrix<TOdfPixelType> & mat, float sigma)
+::Threshold(vnl_matrix<double> & mat, float sigma)
 {
   for(int i = 0; i < mat.rows(); i++)
   {
     for( int j = 0; j < mat.cols(); j++ ){
 
       if(mat(i,j) < 0)
       {
         mat(i,j) = sigma / 2 ;
       }
       if(0 <= mat(i,j) && mat(i,j) < sigma )
       {
-        mat(i,j) = sigma / 2 + (mat(i,j) * mat(i,j)) / 2 * sigma;
+        mat(i,j) = sigma / 2 + (mat(i,j) * mat(i,j)) / (2 * sigma);
       }
       if( 1 - sigma <= mat(i,j) && mat(i,j) < 1 )
       {
-        mat(i,j) = 1-(sigma/2) - (( 1 - mat(i,j) * mat(i,j) ) / 2 * sigma );
+        mat(i,j) = 1-(sigma/2) - (((1 - mat(i,j)) *( 1 - mat(i,j)))  / (2 * sigma) );
       }
       if( 1 <= mat(i,j) )
       {
         mat(i,j) = 1 - (sigma / 2);
       }
     }
   }
 }
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void itk::DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
-::Projection1( vnl_matrix<TOdfPixelType> & E, float delta )
+::Projection1( vnl_matrix<double> & E, float delta )
 {
 
   const double sF = sqrt(5.0);
 
-  vnl_vector<TOdfPixelType> vOnes(E.rows());
+  vnl_vector<double> vOnes(E.rows());
   vOnes.fill(1.0);
 
-  vnl_matrix<TOdfPixelType> T0(E.rows(), E.cols());
+  vnl_matrix<double> T0(E.rows(), E.cols());
+  T0.fill(0.0);
   vnl_matrix<unsigned char> C(E.rows(), 7);
-  vnl_matrix<TOdfPixelType> A(E.rows(), 7);
-  vnl_matrix<TOdfPixelType> B(E.rows(), 7);
-
-  vnl_vector<TOdfPixelType> s0(E.rows());
-  vnl_vector<TOdfPixelType> a0(E.rows());
-  vnl_vector<TOdfPixelType> b0(E.rows());
-  vnl_vector<TOdfPixelType> ta(E.rows());
-  vnl_vector<TOdfPixelType> tb(E.rows());
-  vnl_vector<TOdfPixelType> e(E.rows());
-  vnl_vector<TOdfPixelType> m(E.rows());
-  vnl_vector<TOdfPixelType> a(E.rows());
-  vnl_vector<TOdfPixelType> b(E.rows());
-
+  C.fill(0);
+  vnl_matrix<double> A(E.rows(), 7);
+  A.fill(0.0);
+  vnl_matrix<double> B(E.rows(), 7);
+  B.fill(0.0);
+
+  vnl_vector<double> s0(E.rows());
+  s0.fill(0.0);
+  vnl_vector<double> a0(E.rows());
+  a0.fill(0.0);
+  vnl_vector<double> b0(E.rows());
+  b0.fill(0.0);
+  vnl_vector<double> ta(E.rows());
+  ta.fill(0.0);
+  vnl_vector<double> tb(E.rows());
+  tb.fill(0.0);
+  vnl_vector<double> e(E.rows());
+  e.fill(0.0);
+  vnl_vector<double> m(E.rows());
+  m.fill(0.0);
+  vnl_vector<double> a(E.rows());
+  a.fill(0.0);
+  vnl_vector<double> b(E.rows());
+  b.fill(0.0);
 
   // logarithmierung aller werte in E
   for(int i = 0 ; i < E.rows(); i++)
   {
     for(int j = 0 ; j < E.cols(); j++)
     {
       T0(i,j) = -log(E(i,j));
     }
   }
 
+
+  //T0 = -T0.apply(std::log);
+
   // Summeiere Zeilenweise über alle Shells sum = E1+E2+E3
   for(int i = 0 ; i < E.rows(); i++)
   {
     s0[i] = T0(i,0) + T0(i,1) + T0(i,2);
   }
 
 
   for(int i = 0; i < E.rows(); i ++)
   {
     // Alle Signal-Werte auf der Ersten shell E(N,0) normiert auf s0
     a0 = E(i,0) / s0[i];
     // Alle Signal-Werte auf der Zweiten shell E(N,1) normiert auf s0
     b0 = E(i,1) / s0[i];
   }
 
   ta = a0 * 3.0;
   tb = b0 * 3.0;
   e = tb - (ta * 2.0);
   m = (tb *  2.0 ) + ta;
 
   for(int i = 0; i < E.rows(); i++)
   {
     C(i,0) = tb[i] < 1+3*delta && 0.5+1.5*(sF+1)*delta < ta[i] && ta[i] < 1-3* (sF+2) *delta;
     C(i,1) = e[i] <= -1 +3*(2*sF+5)* delta && ta[i] >= 1-3*(sF+2)*delta;
     C(i,2) = m[i] > 3 -3*sF*delta && -1+3*(2*sF+5)*delta < e[i] && e[i]<-3*sF*delta;
     C(i,3) = m[i] >= 3-3*sF*delta && e[i] >= -3 *sF * delta;
     C(i,4) = 2.5 + 1.5*(5+sF)*delta < m[i] && m[i] < 3-3*sF*delta && e[i] > -3*sF*delta;
-    C(i,5) = ta[i] <= 0.5+1.5 *delta*(sF+1)*delta && m[i] <= 2.5 + 1.5 *(5+sF) * delta;
+    C(i,5) = ta[i] <= 0.5+1.5 *(sF+1)*delta && m[i] <= 2.5 + 1.5 *(5+sF) * delta;
     C(i,6) = !( C(i,0) || C(i,1) || C(i,2) || C(i,3) || C(i,4) || C(i,5) ); // ~ANY(C(i,[0-5] ),2)
   }
 
   A.set_column(0, a0);
   A.set_column(1, vOnes * (1/3 - (sF+2) * delta ));
-  A.set_column(2, (a0*0.8)+ 0.2 - (b0 * 0.4) -delta/sF);
-  A.set_column(3, vOnes * (0.2 + delta /sF));
-  A.set_column(4, a0 * 0.2 + (b0 * 0.4) + 2*delta/sF);
+  A.set_column(2, (a0*0.8)+ 0.2 - (b0 * 0.4) - (delta/sF));
+  A.set_column(3, vOnes * (0.2 + (delta /sF)));
+  A.set_column(4, (a0 * 0.2) + (b0 * 0.4) + 2*(delta/sF));
   A.set_column(5, vOnes * (1/6+0.5*(sF+1)*delta));
   A.set_column(6, a0);
 
 
-  B.set_column(0, (1/3 +delta) * vOnes );
-  B.set_column(1, (1/3 +delta) * vOnes );
-  B.set_column(2, (-(a0 * 0.4)) + 0.4 +  ((b0 * 0.2) - 2*delta/sF) ); //FLAG
+  B.set_column(0, vOnes * (1/3 +delta) );
+  B.set_column(1, vOnes * (1/3 +delta) );
+  B.set_column(2, (-(a0 * 0.4)) + 0.4 +  ((b0 * 0.2) - 2*(delta/sF)) ); //FLAG
   B.set_column(3, vOnes * (0.4 - 3* delta / sF));
-  B.set_column(4, a0 * 0.4 + (b0 * 0.8) - delta /sF);
-  B.set_column(5, vOnes * (1/3+delta));
+  B.set_column(4, (a0 * 0.4) + (b0 * 0.8) - delta /sF);
+  B.set_column(5, vOnes * (1/3 +delta) );
   B.set_column(6, b0 );
 
   for(int i = 0 ; i < E.rows(); i++)
   {
     double sumA = 0;
     double sumB = 0;
     for(int j = 0 ; j < 7; j++)
     {
-      if(C(i,j))
+      if(C(i,j) != 0)
       {
         sumA += A(i,j);
-        sumB +=  B(i,j);
+        sumB += B(i,j);
       }
     }
     a[i] = sumA;
     b[i] = sumB;
   }
 
   for(int i = 0; i < E.rows(); i++)
   {
     E(i,0) = exp(-(a[i]*s0[i]));
     E(i,1) = exp(-(b[i]*s0[i]));
     E(i,2) = exp(-((1-a[i]-b[i])*s0[i]));
   }
+
 }
 
 
-double pow2(double val)
-{
-  return val * val;
-}
 
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void itk::DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
-::Projection2( vnl_vector<TOdfPixelType> & A, vnl_vector<TOdfPixelType> & a, vnl_vector<TOdfPixelType> & b, float delta0)
+::Projection2( vnl_vector<double> & A, vnl_vector<double> & a, vnl_vector<double> & b, float delta0)
 {
 
   const double s6 = sqrt(6);
   const double s15 = s6/2;
 
-  vnl_vector<TOdfPixelType> delta(a.size());
+  vnl_vector<double> delta(a.size());
   delta.fill(delta0);
 
-  vnl_matrix<TOdfPixelType> AM(a.size(), 15);
-  vnl_matrix<TOdfPixelType> aM(a.size(), 15);
-  vnl_matrix<TOdfPixelType> bM(a.size(), 15);
+  vnl_matrix<double> AM(a.size(), 15);
+  AM.fill(0.0);
+  vnl_matrix<double> aM(a.size(), 15);
+  aM.fill(0.0);
+  vnl_matrix<double> bM(a.size(), 15);
+  bM.fill(0.0);
 
   vnl_matrix<unsigned char> B(a.size(), 15);
+  B.fill(0);
 
 
   AM.set_column(0, A);
   AM.set_column(1, A);
   AM.set_column(2, A);
   AM.set_column(3, delta);
-  AM.set_column(4, (A+a-b) - (delta*s6));
+  AM.set_column(4, (A+a-b - (delta*s6))/3);
   AM.set_column(5, delta);
   AM.set_column(6, delta);
   AM.set_column(7, delta);
   AM.set_column(8, A);
-  AM.set_column(9, (a*2+A - ( delta * (2 * (s6 + 1)) ))*0.2);
-  AM.set_column(10, ((b*(-2)) + (A + 2) + (- delta * (2 * (s6 +1) ) ) ) *0.2);
+  AM.set_column(9, (a*2 + A - ( delta * (2 * (s6 + 1)) ))*0.2);
+  AM.set_column(10, ((b*(-2)) + (A + 2) -  delta * (2 * (s6 +1) ) ) *0.2);
   AM.set_column(11, delta);
   AM.set_column(12, delta);
   AM.set_column(13, delta);
   AM.set_column(14, (delta * (-(1 + s15))) + 0.5 );
 
 
   aM.set_column(0, a);
   aM.set_column(1, a);
   aM.set_column(2, -delta + 1);
   aM.set_column(3, a);
   aM.set_column(4, ((A * 2) + (a * 5) + ( b ) + (delta * s6)) / 6);
   aM.set_column(5, a);
   aM.set_column(6, -delta + 1);
   aM.set_column(7, ((a+b) * 0.5) + (delta * (1 + s15)));
   aM.set_column(8, -delta + 1);
   aM.set_column(9, ( (a * 4) + (A * 2) + (delta * (s6 + 1)) )*0.2);
   aM.set_column(10, -delta + 1);
   aM.set_column(11, delta*(s6 +3));
   aM.set_column(12, -delta + 1);
   aM.set_column(13, -delta + 1);
   aM.set_column(14, -delta + 1);
 
   bM.set_column(0, b);
   bM.set_column(1, delta);
   bM.set_column(2, b);
   bM.set_column(3, b);
   bM.set_column(4, (( A * (-2) ) + a + ( b * 5 ) - ( delta* s6 )  ) / 6);
   bM.set_column(5, delta);
   bM.set_column(6, b);
   bM.set_column(7, ((a+b) * 0.5) - (delta * (1 + s15)));
   bM.set_column(8, delta);
   bM.set_column(9, delta);
-  bM.set_column(10, ( (b * 4) - (A * 2) + ((- (delta * (s6 + 1))) + 1) )*0.2);
+  bM.set_column(10, ( (b * 4) - (A * 2) + 1 + (- (delta * (s6 + 1))) )*0.2);
   bM.set_column(11, delta);
   bM.set_column(12, delta);
   bM.set_column(13, (- (delta * (s6 + 3)))  + 1);
   bM.set_column(14, delta);
 
   delta0 *= 0.99;
 
   for(int i = 0 ; i < a.size(); i ++)
   {
     for(int j = 0 ; j < 15; j ++)
     {
       B(i,j) = delta0 < AM(i,j) && 2 * (AM(i,j) + delta0 * s15) < aM(i,j) - bM(i,j) && bM(i,j) > delta0 && aM(i,j) < 1- delta0;
     }
   }
 
-  vnl_matrix<TOdfPixelType> R2(a.size(), 15);
-  vnl_matrix<TOdfPixelType> A_(a.size(), 15);
-  vnl_matrix<TOdfPixelType> a_(a.size(), 15);
-  vnl_matrix<TOdfPixelType> b_(a.size(), 15);
+  vnl_matrix<double> R2(a.size(), 15);
+  R2.fill(0.0);
+  vnl_matrix<double> A_(a.size(), 15);
+  A_.fill(0.0);
+  vnl_matrix<double> a_(a.size(), 15);
+  a_.fill(0.0);
+  vnl_matrix<double> b_(a.size(), 15);
+  b_.fill(0.0);
 
 
 
-  vnl_matrix<TOdfPixelType> OnesVecMat(1, 15);
+  vnl_matrix<double> OnesVecMat(1, 15);
   OnesVecMat.fill(1.0);
 
-  vnl_matrix<TOdfPixelType> AVecMat(a.size(), 1);
+  vnl_matrix<double> AVecMat(a.size(), 1);
   AVecMat.set_column(0,A);
 
-  vnl_matrix<TOdfPixelType> aVecMat(a.size(), 1);
+  vnl_matrix<double> aVecMat(a.size(), 1);
   aVecMat.set_column(0,a);
 
-  vnl_matrix<TOdfPixelType> bVecMat(a.size(), 1);
+  vnl_matrix<double> bVecMat(a.size(), 1);
   bVecMat.set_column(0,b);
 
   A_ = AM - (AVecMat * OnesVecMat);
   a_ = aM - (aVecMat * OnesVecMat);
   b_ = bM - (bVecMat * OnesVecMat);
 
   for(int i = 0 ; i < a.size(); i++)
     for(int j = 0 ; j < 15; j++)
     {
       A_(i,j) *= A_(i,j);
       a_(i,j) *= a_(i,j);
       b_(i,j) *= b_(i,j);
     }
 
 
   R2 = A_ + a_ + b_;
 
   for(int i = 0 ; i < a.size(); i ++)
   {
     for(int j = 0 ; j < 15; j ++)
     {
       if(B(i,j) == 0) R2(i,j) = 9999;
     }
   }
 
   std::vector<unsigned int> indicies(a.size());
 
   // suche den spalten-index der zu der kleinsten Zahl einer Zeile korrespondiert
   for(int i = 0 ; i < a.size(); i++)
   {
     unsigned int index = 0;
     double minvalue = 999;
     for(int j = 0 ; j < 15 ; j++)
     {
       if(R2(i,j) < minvalue){
         minvalue = R2(i,j);
         index = j;
       }
     }
     indicies[i] = index;
   }
 
   for(int i = 0 ; i < a.size(); i++)
   {
     A[i] = AM(i,indicies[i]);
     a[i] = aM(i,indicies[i]);
     b[i] = bM(i,indicies[i]);
   }
 }
 
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void itk::DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
 ::S_S0Normalization( vnl_vector<TOdfPixelType> & vec, typename NumericTraits<ReferencePixelType>::AccumulateType b0 )
 {
 
   double b0f = (double)b0;
   for(int i = 0; i < vec.size(); i++)
   {
     if (b0f==0)
       b0f = 0.01;
-    //if(vec[i] >= b0f)
-    //  vec[i] = b0f - 0.001;
+    if(vec[i] >= b0f)
+      vec[i] = b0f - 0.001;
     vec[i] /= b0f;
   }
 
 }
 
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void itk::DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
-::S_S0Normalization( vnl_matrix<TOdfPixelType> & mat, typename NumericTraits<ReferencePixelType>::AccumulateType b0 )
+::S_S0Normalization( vnl_matrix<double> & mat, typename NumericTraits<ReferencePixelType>::AccumulateType b0 )
 {
   double b0f = (double)b0;
   for(int i = 0; i < mat.rows(); i++)
   {
     for( int j = 0; j < mat.cols(); j++ ){
       if (b0f==0)
         b0f = 0.01;
-      //if(mat(i,j) >= b0f)
-      //  mat(i,j) = b0f - 0.001;
+      if(mat(i,j) >= b0f)
+        mat(i,j) = b0f - 0.001;
       mat(i,j) /= b0f;
     }
   }
 }
 
 
 
+template< class T, class TG, class TO, int L, int NODF>
+void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
+::DoubleLogarithm(vnl_vector<double> & vec)
+{
+  for(int i = 0; i < vec.size(); i++)
+  {
+    vec[i] = log(-log(vec[i]));
+  }
+}
+
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::DoubleLogarithm(vnl_vector<TO> & vec)
 {
   for(int i = 0; i < vec.size(); i++)
   {
     vec[i] = log(-log(vec[i]));
   }
 }
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::SetGradientImage( GradientDirectionContainerType *gradientDirection, const GradientImagesType *gradientImage )
 {
   this->m_GradientDirectionContainer = gradientDirection;
   this->m_NumberOfBaselineImages = 0;
 
   for(GradientDirectionContainerType::Iterator it = gradientDirection->Begin(); it != gradientDirection->End(); it++)
   {
     if( it.Value().one_norm() <= 0.0 )
       this->m_NumberOfBaselineImages ++;
     else
       it.Value() = it.Value() / it.Value().two_norm();
   }
 
   this->m_NumberOfGradientDirections = gradientDirection->Size() - this->m_NumberOfBaselineImages;
 
   // ensure that the gradient image we received has as many components as
   // the number of gradient directions
   if( gradientImage->GetVectorLength() != this->m_NumberOfBaselineImages + m_NumberOfGradientDirections )
   {
     itkExceptionMacro( << m_NumberOfGradientDirections << " gradients + " << this->m_NumberOfBaselineImages
                        << "baselines = " << m_NumberOfGradientDirections + this->m_NumberOfBaselineImages
                        << " directions specified but image has " << gradientImage->GetVectorLength()
                        << " components.");
   }
 
   this->ProcessObject::SetNthInput( 0, const_cast< GradientImagesType* >(gradientImage) );
 }
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::BeforeThreadedGenerateData()
 {
   if( m_NumberOfGradientDirections < (((L+1)*(L+2))/2) /* && m_NumberOfGradientDirections < 6 */ )
   {
     itkExceptionMacro( << "At least " << ((L+1)*(L+2))/2 << " gradient directions are required" );
   }
   // Input must be an itk::VectorImage.
   std::string gradientImageClassName(this->ProcessObject::GetInput(0)->GetNameOfClass());
 
   if ( strcmp(gradientImageClassName.c_str(),"VectorImage") != 0 )
     itkExceptionMacro( << "There is only one Gradient image. I expect that to be a VectorImage. But its of type: " << gradientImageClassName );
 
   m_BZeroImage = BZeroImageType::New();
   typename GradientImagesType::Pointer img = static_cast< GradientImagesType * >( this->ProcessObject::GetInput(0) );
   m_BZeroImage->SetSpacing( img->GetSpacing() );   // Set the image spacing
   m_BZeroImage->SetOrigin( img->GetOrigin() );     // Set the image origin
   m_BZeroImage->SetDirection( img->GetDirection() );  // Set the image direction
   m_BZeroImage->SetLargestPossibleRegion( img->GetLargestPossibleRegion());
   m_BZeroImage->SetBufferedRegion( img->GetLargestPossibleRegion() );
   m_BZeroImage->Allocate();
 
   // if no GradientIndexMap is set take all Gradients to GradientIndicies-Vector and add this to GradientIndexMap[ALL]
   // add All Bzero
   if(m_GradientIndexMap.size() == 0){
 
     // split for all gradient directions the image in baseline-indicies and gradient-indicies for a fast access
     GradientDirectionContainerType::ConstIterator gdcit;
     for( gdcit = this->m_GradientDirectionContainer->Begin(); gdcit != this->m_GradientDirectionContainer->End(); ++gdcit)
     {
       if(gdcit.Value().one_norm() <= 0.0) // if vector length at current position is 0, it is a basline signal
       {
         m_GradientIndexMap[0].push_back(gdcit.Index());
       }else{ // it is a gradient signal of length != 0
         m_GradientIndexMap[1].push_back(gdcit.Index());
       }
     }
     m_ReconstructionType = Standard1Shell;
   }else if(m_GradientIndexMap.size() == 4){
 
     GradientIndexMapIteraotr it = m_GradientIndexMap.begin();
     it++;
     int b1 = (*it).first;
     it++;
     int b2 = (*it).first;
     it++;
     int b3 = (*it).first;
 
 
     if(b2 - b1 == b1 && b3 - b2 == b1 )
     {
       m_ReconstructionType = Analytical3Shells;
     }else
     {
       m_ReconstructionType = NumericalNShells;
     }
   }else if(m_GradientIndexMap.size() > 2)
   {
     m_ReconstructionType = NumericalNShells;
   }
 
 
 
   switch(m_ReconstructionType)
   {
   case Analytical3Shells:
   {
     GradientIndexMapIteraotr it = m_GradientIndexMap.begin();
     it++;
     this->ComputeReconstructionMatrix((*it).second);
     break;
   }
   case NumericalNShells:
     this->ComputeReconstructionMatrix(m_GradientIndexMap[1]);
     break;
   case Standard1Shell:
     this->ComputeReconstructionMatrix(m_GradientIndexMap[1]);
     break;
   }
 
 
 }
 
 
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::StandardOneShellReconstruction(const OutputImageRegionType& outputRegionForThread)
 {
   // Get output image pointer
   typename OutputImageType::Pointer outputImage = static_cast< OutputImageType * >(this->ProcessObject::GetOutput(0));
 
   // ImageRegionIterator for the output image
   ImageRegionIterator< OutputImageType > oit(outputImage, outputRegionForThread);
   oit.GoToBegin();
 
   // ImageRegionIterator for the BZero (output) image
   ImageRegionIterator< BZeroImageType > oit2(m_BZeroImage, outputRegionForThread);
   oit2.GoToBegin();
 
   IndiciesVector BZeroIndicies = m_GradientIndexMap[0];
   IndiciesVector SignalIndicies = m_GradientIndexMap[1];
 
   // if the gradient directiosn aragement is hemispherical, duplicate all gradient directions
   // alone, interested in the value, the direction can be neglected
   if(m_IsHemisphericalArrangementOfGradientDirections){
     int NumbersOfGradientIndicies = SignalIndicies.size();
     for (int i = 0 ; i < NumbersOfGradientIndicies; i++)
       SignalIndicies.push_back(SignalIndicies[i]);
   }
 
 
   // Get input gradient image pointer
   typename GradientImagesType::Pointer gradientImagePointer = static_cast< GradientImagesType * >( this->ProcessObject::GetInput(0) );
 
   //  Const ImageRegionIterator for input gradient image
   typedef ImageRegionConstIterator< GradientImagesType > GradientIteratorType;
   GradientIteratorType git(gradientImagePointer, outputRegionForThread );
   git.GoToBegin();
 
   typedef typename GradientImagesType::PixelType         GradientVectorType;
 
   // iterate overall voxels of the gradient image region
   while( ! git.IsAtEnd() )
   {
     GradientVectorType b = git.Get();
 
     // compute the average bzero signal
     typename NumericTraits<ReferencePixelType>::AccumulateType b0 = NumericTraits<ReferencePixelType>::Zero;
     for(unsigned int i = 0; i < BZeroIndicies.size(); ++i)
     {
       b0 += b[BZeroIndicies[i]];
     }
     b0 /= this->m_NumberOfBaselineImages;
 
     // ODF Vector
     OdfPixelType odf(0.0);
 
     // Create the Signal Vector
     vnl_vector<TO> SignalVector(m_NumberOfGradientDirections);
     if( (b0 != 0) && (b0 >= m_Threshold) )
     {
 
       for( unsigned int i = 0; i< SignalIndicies.size(); i++ )
       {
         SignalVector[i] = static_cast<TO>(b[SignalIndicies[i]]);
       }
 
       // apply threashold an generate ln(-ln(E)) signal
       // Replace SignalVector with PreNormalized SignalVector
       S_S0Normalization(SignalVector, b0);
       DoubleLogarithm(SignalVector);
 
       // ODF coeffs-vector
       vnl_vector<TO> coeffs(m_NumberCoefficients);
 
       // approximate ODF coeffs
       coeffs = ( (*m_CoeffReconstructionMatrix) * SignalVector );
       coeffs[0] += 1.0/(2.0*sqrt(QBALL_ANAL_RECON_PI));
 
       odf = ( (*m_ODFSphericalHarmonicBasisMatrix) * coeffs ).data_block();
     }
     // set ODF to ODF-Image
     oit.Set( odf );
     ++oit;
 
     // set b0(average) to b0-Image
     oit2.Set( b0 );
     ++oit2;
 
     ++git;
 
   }
 
   MITK_INFO << "One Thread finished reconstruction";
 }
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::AnalyticalThreeShellReconstruction(const OutputImageRegionType& outputRegionForThread)
 {
 
   int wrongODF = 0;
 
   typename OutputImageType::Pointer outputImage = static_cast< OutputImageType * >(this->ProcessObject::GetOutput(0));
 
   ImageRegionIterator< OutputImageType > oit(outputImage, outputRegionForThread);
   oit.GoToBegin();
 
   ImageRegionIterator< BZeroImageType > oit2(m_BZeroImage, outputRegionForThread);
   oit2.GoToBegin();
 
   IndiciesVector BZeroIndicies = m_GradientIndexMap[0];
 
   GradientIndexMapIteraotr it = m_GradientIndexMap.begin();
   it++;
   IndiciesVector Shell1Indiecies = (*it).second;
   it++;
   IndiciesVector Shell2Indiecies = (*it).second;
   it++;
   IndiciesVector Shell3Indiecies = (*it).second;
 
 
 
 
 
   //assert(Shell1Indiecies.size() == Shell2Indiecies.size() && Shell1Indiecies.size() == Shell3Indiecies.size());
 
   if(m_IsHemisphericalArrangementOfGradientDirections){
     int NumbersOfGradientIndicies = Shell1Indiecies.size();
     for (int i = 0 ; i < NumbersOfGradientIndicies; i++){
       Shell1Indiecies.push_back(Shell1Indiecies[i]);
       Shell2Indiecies.push_back(Shell2Indiecies[i]);
       Shell3Indiecies.push_back(Shell3Indiecies[i]);
     }
   }
 
   // Get input gradient image pointer
   typename GradientImagesType::Pointer gradientImagePointer = static_cast< GradientImagesType * >( this->ProcessObject::GetInput(0) );
 
   //  Const ImageRegionIterator for input gradient image
   typedef ImageRegionConstIterator< GradientImagesType > GradientIteratorType;
   GradientIteratorType git(gradientImagePointer, outputRegionForThread );
   git.GoToBegin();
 
   typedef typename GradientImagesType::PixelType         GradientVectorType;
 
 
 
   //------------------------- Preperations Zone ------------------------------------
-  vnl_matrix<TO> SHBasis(m_SHBasisMatrix->rows(),m_SHBasisMatrix->cols());
-  SHBasis.fill(0.0);
-
-  for(int i=0; i<SHBasis.rows(); i++)
-    for(unsigned int j=0; j<SHBasis.cols(); j++)
-      SHBasis(i,j) = (TO)((*m_SHBasisMatrix)(i,j));
-
-
-  vnl_matrix<TO> ReconstructionMatrix(m_SignalReonstructionMatrix->rows(),m_SignalReonstructionMatrix->cols());
-  ReconstructionMatrix.fill(0.0);
-
-  for(int i=0; i < ReconstructionMatrix.rows(); i++)
-    for(unsigned int j=0; j < ReconstructionMatrix.cols(); j++)
-      ReconstructionMatrix(i,j) = (TO)(*m_SignalReonstructionMatrix)(i,j);
 
   // N x 3
 
   //  x E1 , E2 , E3
   //
   //  N :    :    :
   //    :    :    :
   //
-  vnl_matrix< TO > E(Shell1Indiecies.size(), 3);
+  vnl_matrix< double > E(Shell1Indiecies.size(), 3);
+
 
-  vnl_vector< TO > Shell1OriginalSignal(Shell1Indiecies.size()); //E1
-  vnl_vector< TO > Shell2OriginalSignal(Shell1Indiecies.size()); //E2
-  vnl_vector< TO > Shell3OriginalSignal(Shell1Indiecies.size()); //E3
+  vnl_vector< double > Shell1OriginalSignal(Shell1Indiecies.size()); //E1
+  vnl_vector< double > Shell2OriginalSignal(Shell1Indiecies.size()); //E2
+  vnl_vector< double > Shell3OriginalSignal(Shell1Indiecies.size()); //E3
 
 
-  vnl_vector< TO > Shell2Coefficients(Shell1Indiecies.size());
-  vnl_vector< TO > Shell3Coefficients(Shell1Indiecies.size());
+  vnl_vector< double > Shell2Coefficients(Shell1Indiecies.size());
+  vnl_vector< double > Shell3Coefficients(Shell1Indiecies.size());
 
-  vnl_vector< TO > SHApproximatedSignal2(Shell1Indiecies.size());
-  vnl_vector< TO > SHApproximatedSignal3(Shell1Indiecies.size());
+  vnl_vector< double > SHApproximatedSignal2(Shell1Indiecies.size());
+  vnl_vector< double > SHApproximatedSignal3(Shell1Indiecies.size());
 
 
 
   //------------------------- Preperations Zone END ---------------------------------
 
 
 
   // iterate overall voxels of the gradient image region
   while( ! git.IsAtEnd() )
   {
+    E.fill(0.0);
+
     Shell1OriginalSignal.fill(0.0);
     Shell2OriginalSignal.fill(0.0);
     Shell3OriginalSignal.fill(0.0);
 
     SHApproximatedSignal3.fill(0.0);
     SHApproximatedSignal2.fill(0.0);
 
     Shell2Coefficients.fill(0.0);
     Shell3Coefficients.fill(0.0);
 
     GradientVectorType b = git.Get();
 
     // compute the average bzero signal
     typename NumericTraits<ReferencePixelType>::AccumulateType b0 = NumericTraits<ReferencePixelType>::Zero;
     for(unsigned int i = 0; i < BZeroIndicies.size(); ++i)
     {
       b0 += b[BZeroIndicies[i]];
     }
     b0 /= this->m_NumberOfBaselineImages;
 
     // ODF Vector
     OdfPixelType odf(0.0);
 
     // Create the Signal Vector
     //vnl_vector<TO> SignalVector(m_NumberOfGradientDirections);
     if( (b0 != 0) && (b0 >= m_Threshold) )
     {
 
       for(int i = 0 ; i < Shell2Indiecies.size(); i++)
       {
-        Shell1OriginalSignal[i] = static_cast<TO>(b[Shell1Indiecies[i]]);
-        Shell2OriginalSignal[i] = static_cast<TO>(b[Shell2Indiecies[i]]);
-        Shell3OriginalSignal[i] = static_cast<TO>(b[Shell3Indiecies[i]]);
+        Shell1OriginalSignal[i] = static_cast<double>(b[Shell1Indiecies[i]]);
+        Shell2OriginalSignal[i] = static_cast<double>(b[Shell2Indiecies[i]]);
+        Shell3OriginalSignal[i] = static_cast<double>(b[Shell3Indiecies[i]]);
       }
 
 
 
-      Shell2Coefficients = ReconstructionMatrix * Shell2OriginalSignal;
-      Shell3Coefficients = ReconstructionMatrix * Shell3OriginalSignal;
+      Shell2Coefficients = (*m_SignalReonstructionMatrix) * Shell2OriginalSignal;
+      Shell3Coefficients = (*m_SignalReonstructionMatrix) * Shell3OriginalSignal;
 
-      SHApproximatedSignal2 = SHBasis * Shell2Coefficients;
-      SHApproximatedSignal3 = SHBasis * Shell3Coefficients;
+      SHApproximatedSignal2 = (*m_SHBasisMatrix) * Shell2Coefficients;
+      SHApproximatedSignal3 = (*m_SHBasisMatrix) * Shell3Coefficients;
 
 
       E.set_column(0,Shell1OriginalSignal);
       E.set_column(1,SHApproximatedSignal2);
       E.set_column(2,SHApproximatedSignal3);
 
 
       // Si/S0
       S_S0Normalization(E,b0);
 
-
       //MITK_INFO << E;
       //exit(0);
 
       //Implements Eq. [19] and Fig. 4.
-      Threshold(E, m_Lambda);
+      Threshold(E);
 
       //inqualities [31]. Taking the lograithm of th first tree inqualities
       //convert the quadratic inqualities to linear ones.
-      Projection1(E, m_Lambda);
-
-
-      vnl_vector<TO> AlphaValues(Shell1Indiecies.size());
-      vnl_vector<TO> BetaValues(Shell1Indiecies.size());
-      vnl_vector<TO> LAValues(Shell1Indiecies.size());
-      vnl_vector<TO> PValues(Shell1Indiecies.size());
+      Projection1(E);
 
 
+      vnl_vector<double> AlphaValues(Shell1Indiecies.size());
+      AlphaValues.fill(0.0);
+      vnl_vector<double> BetaValues(Shell1Indiecies.size());
+      BetaValues.fill(0.0);
+      vnl_vector<double> LAValues(Shell1Indiecies.size());
+      LAValues.fill(0.0);
+      vnl_vector<double> PValues(Shell1Indiecies.size());
+      PValues.fill(0.0);
 
+      // try this
+      //Threshold(E, m_Lambda);
 
 
       for( unsigned int i = 0; i< Shell1Indiecies.size(); i++ )
       {
 
 
 
-        TO E1 = E(i,0);
-        TO E2 = E(i,1);
-        TO E3 = E(i,2);
+        double E1 = E(i,0);
+        double E2 = E(i,1);
+        double E3 = E(i,2);
 
         /* if(!(0 < E3) || (!(E3 < E2)) || (!(E2 < E1)) || (!(E1 < 1)) || (!(E1 * E1 < E2)) || (!(E2 * E2 < E1 * E3)) || (!(E3-E1*E2 < E2 - E1*E1 + E1*E3 -E2*E2)))
         {
           MITK_INFO << "0 < " << E3 << " < " << E2 << " < " << E1 << " < 1 " ;
 
         }*/
 
-        TO P2 = E2-E1*E1;
-        TO A = (E3 -E1*E2) / ( 2* P2);
-        TO B2 = A * A -(E1 * E3 - E2 * E2) /P2;
-        TO B = 0;
+        double P2 = E2-E1*E1;
+        double A = (E3 -E1*E2) / ( 2* P2);
+        double B2 = A * A -(E1 * E3 - E2 * E2) /P2;
+        double B = 0;
         if(B2 > 0) B = sqrt(B2);
 
+        double P = 0;
+        if(P2 > 0) P = sqrt(P2);
+        PValues[i] = P;
 
-          TO P = 0;
-          if(P2 > 0) P = sqrt(P2);
-          PValues[i] = P;
-
-
-        TO alpha = A + B;
-        TO beta = A - B;
+        double alpha = A + B;
+        double beta = A - B;
         AlphaValues[i] = alpha;
         BetaValues[i] = beta;
 
-        TO lambda = 0.5 + 0.5 * sqrt(1-(((2*P)/(alpha - beta)) * ((2*P)/(alpha - beta))) );
+
+        double lambda = 0.5 + ((E1-A)/(2*B));
+        if(lambda != lambda)
+        {
+          MITK_INFO << "FAIL";
+          MITK_INFO << "E1 : " << E1 << "  E2 : " << E2 << "  E3 : " << E3;
+          MITK_INFO << E;
+          MITK_INFO << A;
+          MITK_INFO << B;
+          MITK_INFO << alpha;
+          MITK_INFO << beta;
+        }
+        /*TO lambda = 0.5 + 0.5 * sqrt(1-(((2*P)/(alpha - beta)) * ((2*P)/(alpha - beta))) );
+        if(lambda != lambda) MITK_INFO << "FAIL";
         TO ER1 = std::fabs(lambda * (alpha - beta) + beta - E1) + std::fabs(lambda * (alpha * alpha - beta * beta) + beta * beta - E2) + std::fabs(lambda * (alpha * alpha * alpha - beta * beta * beta) + beta* beta *beta - E3 );
         TO ER2 = std::fabs((1-lambda) * (alpha - beta) + beta - E1) + std::fabs((1-lambda) * (alpha * alpha - beta * beta) + beta * beta - E2) + std::fabs((1-lambda) * (alpha * alpha * alpha - beta * beta * beta) + beta* beta *beta - E3 );
         // Needed for Projection 2
 
         if(ER1 < ER2) LAValues[i] = lambda;
-        if(ER1 >= ER2) LAValues[i] = 1 - lambda;
+        if(ER1 >= ER2) LAValues[i] = 1 - lambda;*/
+
+        LAValues[i] = lambda;
+
       }
 
-      Projection2(PValues, AlphaValues, BetaValues, m_Lambda);
+      Projection2(PValues, AlphaValues, BetaValues);
 
       Threshold(AlphaValues);
       Threshold(BetaValues);
 
       DoubleLogarithm(AlphaValues);
       DoubleLogarithm(BetaValues);
 
       vnl_vector<TO> SignalVector(Shell1Indiecies.size());
+      SignalVector.fill(0.0);
       for( unsigned int i = 0; i< AlphaValues.size(); i++ )
       {
-        SignalVector = LAValues[i] * AlphaValues + LAValues[i] * BetaValues;
+        SignalVector[i] = static_cast<TO>(LAValues[i] * AlphaValues[i] + LAValues[i] * BetaValues[i]);
       }
 
       vnl_vector<TO> coeffs(m_NumberCoefficients);
+      coeffs.fill(0.0);
 
       coeffs = ( (*m_CoeffReconstructionMatrix) * SignalVector );
       coeffs[0] += 1.0/(2.0*sqrt(QBALL_ANAL_RECON_PI));
 
       odf = ( (*m_ODFSphericalHarmonicBasisMatrix) * coeffs ).data_block();
     }
     // set ODF to ODF-Image
     oit.Set( odf );
     ++oit;
     // MITK_INFO << odf;
     // set b0(average) to b0-Image
     oit2.Set( b0 );
     ++oit2;
 
     ++git;
 
   }
 
 
+
+
   MITK_INFO << "THREAD FINISHED with " << wrongODF << " WrongODFs";
 
 
 }
 
 
 template< class T, class TG, class TO, int L, int NODF>
 vnl_vector<TO> DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::AnalyticalThreeShellParameterEstimation(const IndiciesVector * shell1Indicies,const IndiciesVector * shell2Indicies ,const IndiciesVector * shell3Indicies, vnl_vector<TO>)
 {
 
 
 
 }
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int NumberOfThreads)
 {
   switch(m_ReconstructionType)
   {
   case Standard1Shell:
     StandardOneShellReconstruction(outputRegionForThread);
     break;
   case Analytical3Shells:
     AnalyticalThreeShellReconstruction(outputRegionForThread);
     break;
   case NumericalNShells:
     break;
   }
 
 
 
 }
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T, TG, TO, L, NODF>::
 ComputeSphericalHarmonicsBasis(vnl_matrix<double> * QBallReference, vnl_matrix<double> *SHBasisOutput, vnl_matrix<double>* LaplaciaBaltramiOutput, vnl_vector<int>* SHOrderAssociation, vnl_matrix<double>* SHEigenvalues )
 {
   for(unsigned int i=0; i< (*SHBasisOutput).rows(); i++)
   {
     for(int k = 0; k <= L; k += 2)
     {
       for(int m =- k; m <= k; m++)
       {
         int j = ( k * k + k + 2 ) / 2 + m - 1;
 
         // Compute SHBasisFunctions
         double phi = (*QBallReference)(0,i);
         double th = (*QBallReference)(1,i);
         (*SHBasisOutput)(i,j) = mitk::ShericalHarmonicsFunctions::Yj(m,k,th,phi);
 
         // Laplacian Baltrami Order Association
         if(LaplaciaBaltramiOutput)
           (*LaplaciaBaltramiOutput)(j,j) = k*k*(k + 1)*(k+1);
 
         // SHEigenvalues with order Accosiation kj
         if(SHEigenvalues)
           (*SHEigenvalues)(j,j) = -k* (k+1);
 
         // Order Association
         if(SHOrderAssociation)
           (*SHOrderAssociation)[j] = k;
 
       }
     }
   }
 }
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::ComputeFunkRadonTransformationMatrix(vnl_vector<int>* SHOrderAssociationReference, vnl_matrix<double>* FRTMatrixOutput )
 {
   for(int i=0; i<m_NumberCoefficients; i++)
   {
     (*FRTMatrixOutput)(i,i) = 2.0 * QBALL_ANAL_RECON_PI * mitk::ShericalHarmonicsFunctions::legendre0((*SHOrderAssociationReference)[i]);
   }
 }
 
 template< class T, class TG, class TO, int L, int NOdfDirections>
 bool DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NOdfDirections>
 ::CheckHemisphericalArrangementOfGradientDirections()
 {
   // handle acquisition schemes where only half of the spherical
   // shell is sampled by the gradient directions. In this case,
   // each gradient direction is duplicated in negative direction.
   vnl_vector<double> centerMass(3);
   centerMass.fill(0.0);
   int count = 0;
   GradientDirectionContainerType::ConstIterator gdcit1;
   for( gdcit1 = this->m_GradientDirectionContainer->Begin(); gdcit1 != this->m_GradientDirectionContainer->End(); ++gdcit1)
   {
     if(gdcit1.Value().one_norm() > 0.0)
     {
       centerMass += gdcit1.Value();
       count ++;
     }
   }
   centerMass /= count;
   if(centerMass.two_norm() > 0.1)
   {
     return false;
   }
   return true;
 }
 
 template< class T, class TG, class TO, int L, int NOdfDirections>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NOdfDirections>
 ::ComputeReconstructionMatrix(std::vector< unsigned int > gradientIndiciesVector)
 {
 
   typedef std::auto_ptr< vnl_matrix< double> >  MatrixDoublePtr;
   typedef std::auto_ptr< vnl_vector< int > >    VectorIntPtr;
   typedef std::auto_ptr< vnl_matrix_inverse< double > >  InverseMatrixDoublePtr;
 
   int numberOfGradientDirections = gradientIndiciesVector.size();
 
 
   if( numberOfGradientDirections < (((L+1)*(L+2))/2) || numberOfGradientDirections < 6  )
   {
     itkExceptionMacro( << "At least (L+1)(L+2)/2 gradient directions are required" );
   }
 
   CheckDuplicateDiffusionGradients();
 
 
   // check if gradient directions are arrangement as a hemisphere(true) or sphere(false)
   m_IsHemisphericalArrangementOfGradientDirections = CheckHemisphericalArrangementOfGradientDirections();
   if(m_IsHemisphericalArrangementOfGradientDirections) numberOfGradientDirections *= 2;
 
 
   MatrixDoublePtr Q(new vnl_matrix<double>(3, numberOfGradientDirections));
   Q->fill(0.0);
   // Cartesian to spherical coordinates
   {
     int j = 0;
 
     for(int i = 0; i < gradientIndiciesVector.size(); i++)
     {
 
       double x = this->m_GradientDirectionContainer->ElementAt(gradientIndiciesVector[i]).get(0);
       double y = this->m_GradientDirectionContainer->ElementAt(gradientIndiciesVector[i]).get(1);
       double z = this->m_GradientDirectionContainer->ElementAt(gradientIndiciesVector[i]).get(2);
       double cart[3];
       mitk::ShericalHarmonicsFunctions::Cart2Sph(x,y,z,cart);
       (*Q)(0,j) = cart[0];
       (*Q)(1,j) = cart[1];
       (*Q)(2,j++) = cart[2];
 
     }
     if(m_IsHemisphericalArrangementOfGradientDirections)
     {
       for(int i = 0; i < gradientIndiciesVector.size(); i++)
       {
         double x = this->m_GradientDirectionContainer->ElementAt(gradientIndiciesVector[i]).get(0);
         double y = this->m_GradientDirectionContainer->ElementAt(gradientIndiciesVector[i]).get(1);
         double z = this->m_GradientDirectionContainer->ElementAt(gradientIndiciesVector[i]).get(2);
         double cart[3];
         mitk::ShericalHarmonicsFunctions::Cart2Sph(x,y,z,cart);
         (*Q)(0,j) = cart[0];
         (*Q)(1,j) = cart[1];
         (*Q)(2,j++) = cart[2];
       }
     }
   }
 
   int LOrder = L;
   m_NumberCoefficients = (int)(LOrder*LOrder + LOrder + 2.0)/2.0 + LOrder;
 
   m_SHBasisMatrix = new vnl_matrix<double>(numberOfGradientDirections,m_NumberCoefficients);
   m_SHBasisMatrix->fill(0.0);
   VectorIntPtr SHOrderAssociation(new vnl_vector<int>(m_NumberCoefficients));
   SHOrderAssociation->fill(0.0);
   MatrixDoublePtr LaplacianBaltrami(new vnl_matrix<double>(m_NumberCoefficients,m_NumberCoefficients));
   LaplacianBaltrami->fill(0.0);
   MatrixDoublePtr FRTMatrix(new vnl_matrix<double>(m_NumberCoefficients,m_NumberCoefficients));
   FRTMatrix->fill(0.0);
   MatrixDoublePtr SHEigenvalues(new vnl_matrix<double>(m_NumberCoefficients,m_NumberCoefficients));
   SHEigenvalues->fill(0.0);
 
 
 
   // SHBasis-Matrix + LaplacianBaltrami-Matrix + SHOrderAssociationVector
   ComputeSphericalHarmonicsBasis(Q.get() ,m_SHBasisMatrix, LaplacianBaltrami.get(), SHOrderAssociation.get(), SHEigenvalues.get());
 
   // Compute FunkRadon Transformation Matrix Associated to SHBasis Order lj
   ComputeFunkRadonTransformationMatrix(SHOrderAssociation.get() ,FRTMatrix.get());
 
   MatrixDoublePtr temp(new vnl_matrix<double>(((m_SHBasisMatrix->transpose()) * (*m_SHBasisMatrix)) + (m_Lambda  * (*LaplacianBaltrami))));
 
   InverseMatrixDoublePtr pseudo_inv(new vnl_matrix_inverse<double>((*temp)));
   MatrixDoublePtr inverse(new vnl_matrix<double>(m_NumberCoefficients,m_NumberCoefficients));
   inverse->fill(0.0);
   (*inverse) = pseudo_inv->inverse();
 
   // ODF Factor ( missing 1/4PI ?? )
   double factor = (1.0/(16.0*QBALL_ANAL_RECON_PI*QBALL_ANAL_RECON_PI));
 
   m_SignalReonstructionMatrix = new vnl_matrix<double>();
   m_SignalReonstructionMatrix->fill(0.0);
   (*m_SignalReonstructionMatrix) = (*inverse) * (m_SHBasisMatrix->transpose());
 
   MatrixDoublePtr TransformationMatrix(new vnl_matrix<double>( factor * ((*FRTMatrix) * ((*SHEigenvalues) * (*m_SignalReonstructionMatrix))) ) );
 
   m_CoeffReconstructionMatrix = new vnl_matrix<TO>(m_NumberCoefficients,numberOfGradientDirections);
 
 
   // Cast double to float
   for(int i=0; i<m_NumberCoefficients; i++)
     for(unsigned int j=0; j<numberOfGradientDirections; j++)
       (*m_CoeffReconstructionMatrix)(i,j) = (float)(*TransformationMatrix)(i,j);
 
 
 
   // this code goes to the image adapter coeffs->odfs later
 
   vnl_matrix_fixed<double, 3, NOdfDirections>* U = itk::PointShell<NOdfDirections, vnl_matrix_fixed<double, 3, NOdfDirections> >::DistributePointShell();
 
   m_ODFSphericalHarmonicBasisMatrix  = new vnl_matrix<TO>(NOdfDirections,m_NumberCoefficients);
 
 
   for(int i=0; i<NOdfDirections; i++)
   {
     double x = (*U)(0,i);
     double y = (*U)(1,i);
     double z = (*U)(2,i);
     double cart[3];
     mitk::ShericalHarmonicsFunctions::Cart2Sph(x,y,z,cart);
     (*U)(0,i) = cart[0];
     (*U)(1,i) = cart[1];
     (*U)(2,i) = cart[2];
   }
 
   for(int i=0; i<NOdfDirections; i++)
   {
     for(int k=0; k<=LOrder; k+=2)
     {
       for(int m=-k; m<=k; m++)
       {
         int j = (k*k + k + 2)/2 + m - 1;
         double phi = (*U)(0,i);
         double th = (*U)(1,i);
         (*m_ODFSphericalHarmonicBasisMatrix)(i,j) = mitk::ShericalHarmonicsFunctions::Yj(m,k,th,phi);
       }
     }
   }
 
   m_ReconstructionMatrix = new vnl_matrix<TO>((*m_ODFSphericalHarmonicBasisMatrix) * (*m_CoeffReconstructionMatrix));
 
 }
 
 template< class T, class TG, class TO, int L, int NODF>
 template< class VNLType >
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::printMatrix( VNLType * mat  )
 {
 
   std::stringstream stream;
 
   for(int i = 0 ; i < mat->rows(); i++)
   {
     stream.str("");
     for(int j = 0; j < mat->cols(); j++)
     {
       stream << (*mat)(i,j) << "  ";
     }
 
   }
 
 
   MITK_INFO << stream.str();
 
 }
 
 template< class T, class TG, class TO, int L, int NODF>
 bool DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::CheckDuplicateDiffusionGradients()
 {
 
   GradientDirectionContainerType::ConstIterator gdcit1;
   GradientDirectionContainerType::ConstIterator gdcit2;
 
   for(gdcit1 = this->m_GradientDirectionContainer->Begin(); gdcit1 != this->m_GradientDirectionContainer->End(); ++gdcit1)
   {
     for(gdcit2 = this->m_GradientDirectionContainer->Begin(); gdcit2 != this->m_GradientDirectionContainer->End(); ++gdcit2)
     {
       if(gdcit1.Value() == gdcit2.Value() && gdcit1.Index() != gdcit2.Index())
       {
         itkWarningMacro( << "Some of the Diffusion Gradients equal each other. Corresponding image data should be averaged before calling this filter." );
         return true;
       }
     }
   }
   return false;
 }
 
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::PrintSelf(std::ostream& os, Indent indent) const
 {
   std::locale C("C");
   std::locale originalLocale = os.getloc();
   os.imbue(C);
 
   Superclass::PrintSelf(os,indent);
 
   os << indent << "OdfReconstructionMatrix: " << m_ReconstructionMatrix << std::endl;
   if ( m_GradientDirectionContainer )
   {
     os << indent << "GradientDirectionContainer: "
        << m_GradientDirectionContainer << std::endl;
   }
   else
   {
     os << indent <<
           "GradientDirectionContainer: (Gradient directions not set)" << std::endl;
   }
   os << indent << "NumberOfGradientDirections: " <<
         m_NumberOfGradientDirections << std::endl;
   os << indent << "NumberOfBaselineImages: " <<
         m_NumberOfBaselineImages << std::endl;
   os << indent << "Threshold for reference B0 image: " << m_Threshold << std::endl;
   os << indent << "BValue: " << m_BValue << std::endl;
 
   os.imbue( originalLocale );
 }
 
 
 
 
 }
 
 #endif // __itkDiffusionMultiShellQballReconstructionImageFilter_cpp
diff --git a/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.h b/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.h
index 9b44546390..2676b32648 100644
--- a/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.h
+++ b/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.h
@@ -1,237 +1,238 @@
 /*=========================================================================
 
 Program:   Medical Imaging & Interaction Toolkit
 Language:  C++
 Date:      $Date: 2009-07-14 19:11:20 +0200 (Tue, 14 Jul 2009) $
 Version:   $Revision: 18127 $
 
 Copyright (c) German Cancer Research Center, Division of Medical and
 Biological Informatics. All rights reserved.
 See MITKCopyright.txt or http://www.mitk.org/copyright.html for details.
 
 This software is distributed WITHOUT ANY WARRANTY; without even
 the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
 PURPOSE.  See the above copyright notices for more information.
 
 =========================================================================*/
 
 #ifndef __itkDiffusionMultiShellQballReconstructionImageFilter_h_
 #define __itkDiffusionMultiShellQballReconstructionImageFilter_h_
 
 #include "itkImageToImageFilter.h"
 #include "vnl/vnl_vector_fixed.h"
 #include "vnl/vnl_matrix.h"
 #include "vnl/algo/vnl_svd.h"
 #include "itkVectorContainer.h"
 #include "itkVectorImage.h"
 #include <iomanip>
 
 
 
 namespace itk{
 /** \class DiffusionMultiShellQballReconstructionImageFilter
     Aganj_2010
  */
 
 template< class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 class DiffusionMultiShellQballReconstructionImageFilter : public ImageToImageFilter< Image< TReferenceImagePixelType, 3 >, Image< Vector< TOdfPixelType, NrOdfDirections >, 3 > >
 {
 
 public:
 
     typedef DiffusionMultiShellQballReconstructionImageFilter Self;
     typedef SmartPointer<Self>                      Pointer;
     typedef SmartPointer<const Self>                ConstPointer;
     typedef ImageToImageFilter< Image< TReferenceImagePixelType, 3>, Image< Vector< TOdfPixelType, NrOdfDirections >, 3 > > Superclass;
     typedef TReferenceImagePixelType                  ReferencePixelType;
     typedef TGradientImagePixelType                   GradientPixelType;
     typedef Vector< TOdfPixelType, NrOdfDirections >  OdfPixelType;
     typedef typename Superclass::InputImageType       ReferenceImageType;
     typedef Image< OdfPixelType, 3 >                  OdfImageType;
     typedef OdfImageType                              OutputImageType;
     typedef TOdfPixelType                             BZeroPixelType;
     typedef Image< BZeroPixelType, 3 >                BZeroImageType;
     typedef typename Superclass::OutputImageRegionType OutputImageRegionType;
     /** Typedef defining one (of the many) gradient images.  */
     typedef Image< GradientPixelType, 3 >            GradientImageType;
     /** An alternative typedef defining one (of the many) gradient images.
    * It will be assumed that the vectorImage has the same dimension as the
    * Reference image and a vector length parameter of \c n (number of
    * gradient directions)*/
     typedef VectorImage< GradientPixelType, 3 >       GradientImagesType;
     /** Holds the ODF reconstruction matrix */
     typedef vnl_matrix< TOdfPixelType >*              OdfReconstructionMatrixType;
     typedef vnl_matrix< double > *                    CoefficientMatrixType;
     /** Holds each magnetic field gradient used to acquire one DWImage */
     typedef vnl_vector_fixed< double, 3 >             GradientDirectionType;
     /** Container to hold gradient directions of the 'n' DW measurements */
     typedef VectorContainer< unsigned int, GradientDirectionType > GradientDirectionContainerType;
 
     typedef std::map<double, std::vector<unsigned int> > GradientIndexMap;
     typedef std::map<double, std::vector<unsigned int> >::iterator GradientIndexMapIteraotr;
     typedef std::vector<unsigned int> IndiciesVector;
 
     // --------------------------------------------------------------------------------------------//
 
     /** Method for creation through the object factory. */
     itkNewMacro(Self);
 
     /** Runtime information support. */
     itkTypeMacro(DiffusionMultiShellQballReconstructionImageFilter, ImageToImageFilter);
 
     /** set method to add gradient directions and its corresponding
    * image. The image here is a VectorImage. The user is expected to pass the
    * gradient directions in a container. The ith element of the container
    * corresponds to the gradient direction of the ith component image the
    * VectorImage.  For the baseline image, a vector of all zeros
    * should be set.*/
     void SetGradientImage( GradientDirectionContainerType *, const GradientImagesType *image);
     void SetGradientIndexMap(GradientIndexMap gradientIdexMap)
     {
 
 
         m_GradientIndexMap = gradientIdexMap;
 
         /*
         std::stringstream s1, s2, s3;
         for(int i = 0; i < m_GradientIndexMap[1000].size() ; i++){
             s1 << m_GradientIndexMap[1000][i] << " ";
             s2 << m_GradientIndexMap[2000][i] << " ";
             s3 << m_GradientIndexMap[3000][i] << " ";
         }
 
         MITK_INFO << "1 SHELL " << std::endl << s1.str();
         MITK_INFO << "2 SHELL " << std::endl << s2.str();
         MITK_INFO << "3 SHELL " << std::endl << s3.str();
         */
 
 
     }
 
     /** Get reference image */
     virtual ReferenceImageType * GetReferenceImage()
     { return ( static_cast< ReferenceImageType *>(this->ProcessObject::GetInput(0)) ); }
 
     /** Return the gradient direction. idx is 0 based */
     virtual GradientDirectionType GetGradientDirection( unsigned int idx) const
     {
         if( idx >= m_GradientDirectionContainer->Size() )
         {
             itkExceptionMacro( << "Gradient direction " << idx << "does not exist" );
         }
         return m_GradientDirectionContainer->ElementAt( idx+1 );
     }
 
     OdfPixelType Normalize(OdfPixelType odf, typename NumericTraits<ReferencePixelType>::AccumulateType b0 );
 
     void S_S0Normalization( vnl_vector<TOdfPixelType> & vec, typename NumericTraits<ReferencePixelType>::AccumulateType b0  = 0 );
-    void S_S0Normalization( vnl_matrix<TOdfPixelType> & mat, typename NumericTraits<ReferencePixelType>::AccumulateType b0  = 0 );
+    void S_S0Normalization( vnl_matrix<double> & mat, typename NumericTraits<ReferencePixelType>::AccumulateType b0  = 0 );
 
+    void DoubleLogarithm(vnl_vector<double> & vec);
     void DoubleLogarithm(vnl_vector<TOdfPixelType> & vec);
 
-    void Threshold(vnl_vector<TOdfPixelType> & vec, float sigma = 0.0001);
-    void Threshold(vnl_matrix<TOdfPixelType> & mat, float sigma = 0.0001);
+    void Threshold(vnl_vector<double> & vec, float sigma = 0.0001);
+    void Threshold(vnl_matrix<double> & mat, float sigma = 0.0001);
 
-    void Projection1( vnl_matrix<TOdfPixelType> & mat, float delta = 0.0001);
-    void Projection2( vnl_vector<TOdfPixelType> & A, vnl_vector<TOdfPixelType> & alpha, vnl_vector<TOdfPixelType> & beta, float delta = 0.0001);
+    void Projection1( vnl_matrix<double> & mat, float delta = 0.0001);
+    void Projection2( vnl_vector<double> & A, vnl_vector<double> & alpha, vnl_vector<double> & beta, float delta = 0.0001);
 
     /** Threshold on the reference image data. The output ODF will be a null
    * pdf for pixels in the reference image that have a value less than this
    * threshold. */
     itkSetMacro( Threshold, ReferencePixelType );
     itkGetMacro( Threshold, ReferencePixelType );
 
     itkGetMacro( BZeroImage, typename BZeroImageType::Pointer);
     //itkGetMacro( ODFSumImage, typename BlaImage::Pointer);
 
     itkSetMacro( BValue, TOdfPixelType);
 
     itkSetMacro( Lambda, double );
     itkGetMacro( Lambda, double );
 
     itkGetConstReferenceMacro( BValue, TOdfPixelType);
 
 
 protected:
     DiffusionMultiShellQballReconstructionImageFilter();
     ~DiffusionMultiShellQballReconstructionImageFilter() {};
     void PrintSelf(std::ostream& os, Indent indent) const;
 
     void ComputeReconstructionMatrix(std::vector< unsigned int > gradientIndiciesVector);
     bool CheckDuplicateDiffusionGradients();
     void ComputeSphericalHarmonicsBasis(vnl_matrix<double>* QBallReference, vnl_matrix<double>* SHBasisOutput, vnl_matrix<double>* LaplaciaBaltramiOutput, vnl_vector<int>* SHOrderAssociation , vnl_matrix<double> * SHEigenvalues);
     void ComputeFunkRadonTransformationMatrix(vnl_vector<int>* SHOrderAssociationReference, vnl_matrix<double>* FRTMatrixOutput );
     bool CheckHemisphericalArrangementOfGradientDirections();
 
     void BeforeThreadedGenerateData();
     void ThreadedGenerateData( const OutputImageRegionType &outputRegionForThread, int NumberOfThreads );
 
     vnl_vector<TOdfPixelType> AnalyticalThreeShellParameterEstimation(const IndiciesVector * shell1, const IndiciesVector * shell2, const IndiciesVector * shell3, vnl_vector<TOdfPixelType> b);
     void StandardOneShellReconstruction(const OutputImageRegionType& outputRegionForThread);
     void AnalyticalThreeShellReconstruction(const OutputImageRegionType& outputRegionForThread);
 
 private:
 
     enum ReconstructionType
     {
         Analytical3Shells,
         NumericalNShells,
         Standard1Shell
     };
 
 
 
     OdfReconstructionMatrixType m_ReconstructionMatrix;
     OdfReconstructionMatrixType m_CoeffReconstructionMatrix;
     OdfReconstructionMatrixType m_ODFSphericalHarmonicBasisMatrix;
 
 
     CoefficientMatrixType m_SignalReonstructionMatrix;
     CoefficientMatrixType m_SHBasisMatrix;
 
     /** container to hold gradient directions */
     GradientDirectionContainerType::Pointer m_GradientDirectionContainer;
 
     /** Number of gradient measurements */
     unsigned int m_NumberOfGradientDirections;
 
     /** Number of baseline images */
     unsigned int m_NumberOfBaselineImages;
 
     /** Threshold on the reference image data */
     ReferencePixelType m_Threshold;
 
     /** LeBihan's b-value for normalizing tensors */
     TOdfPixelType m_BValue;
 
     typename BZeroImageType::Pointer m_BZeroImage;
 
     GradientIndexMap m_GradientIndexMap;
 
     double m_Lambda;
 
     bool m_IsHemisphericalArrangementOfGradientDirections;
 
     bool m_IsArithmeticProgession;
 
     int m_NumberCoefficients;
 
     ReconstructionType m_ReconstructionType;
 
 
 
     template< class VNLType >
     void printMatrix( VNLType * mat );
 
 
 
 };
 
 }
 
 #ifndef ITK_MANUAL_INSTANTIATION
 #include "itkDiffusionMultiShellQballReconstructionImageFilter.cpp"
 #endif
 
 #endif //__itkDiffusionMultiShellQballReconstructionImageFilter_h_