diff --git a/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.cpp b/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.cpp
index 73989b89f8..205aa22df8 100644
--- a/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.cpp
+++ b/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.cpp
@@ -1,1130 +1,1166 @@
 /*===================================================================
 
 The Medical Imaging Interaction Toolkit (MITK)
 
 Copyright (c) German Cancer Research Center,
 Division of Medical and Biological Informatics.
 All rights reserved.
 
 This software is distributed WITHOUT ANY WARRANTY; without
 even the implied warranty of MERCHANTABILITY or FITNESS FOR
 A PARTICULAR PURPOSE.
 
 See LICENSE.txt or http://www.mitk.org for details.
 
 ===================================================================*/
 #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>
 #include "mitkDiffusionFunctionCollection.h"
 #include "itkPointShell.h"
 #include <memory>
 
 
 namespace itk {
 
 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(Mode_Standard1Shell),
   m_Interpolation_Flag(false),
   m_Interpolation_SHT1_inv(0),
   m_Interpolation_SHT2_inv(0),
   m_Interpolation_SHT3_inv(0),
   m_Interpolation_TARGET_SH(0)
 {
   // 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>
 void DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
 ::Normalize( OdfPixelType  & out)
 {
   for(int i=0; i<NrOdfDirections; i++)
   {
     out[i] = out[i] < 0 ? 0 : out[i];
     out[i] *= M_PI * 4 / NrOdfDirections;
   }
 }
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 double DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
 ::CalculateThreashold(const double value, const double delta)
 {
   return (value<0)*(0.5*delta) + (value>=0 && value<delta)*(0.5*delta+0.5*(value*value)/delta)
       + (value>=delta && value<1-delta)*value+(value>=1-delta && value<1)*(1-0.5*delta-0.5*((1-value)*(1-value))/delta)
       + (value>=1)*(1-0.5*delta);
 }
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
 ::Threshold(vnl_vector<double> & vec, double delta)
 {
   if (delta==0){   //Clip attenuation values. If att<0 => att=0, if att>1 => att=1
     for (int i=0; i<vec.size(); i++)
       vec[i]=(vec[i]>=0 && vec[i]<=1)*vec[i]+(vec[i]>1);
   }
   else{            //Use function from Aganj et al, MRM, 2010
     for (int i=0; i< vec.size(); i++)
       vec[i]=CalculateThreashold(vec[i], delta);
   }
 }
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
 ::Threshold(vnl_matrix<double> & mat, double delta)
 {
   if (delta==0){   //Clip attenuation values. If att<0 => att=0, if att>1 => att=1
     for (int i=0; i<mat.rows(); i++)
       for (int j=0; j<mat.cols(); j++)
         mat(i,j)=(mat(i,j)>=0 && mat(i,j)<=1)*mat(i,j)+(mat(i,j)>1);
   }
   else{            //Use function from Aganj et al, MRM, 2010
     for (int i=0; i<mat.rows(); i++)
       for (int j=0; j<mat.cols(); j++)
         mat(i,j)=CalculateThreashold(mat(i,j), delta);
   }
 }
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
 ::Projection1( vnl_matrix<double> & E, double delta )
 {
 
   const double sF = sqrt(5.0);
 
   vnl_vector<double> vOnes(E.rows());
   vOnes.fill(1.0);
 
   vnl_matrix<double> T0(E.rows(), E.cols());
   vnl_matrix<unsigned char> C(E.rows(), 7);
   vnl_matrix<double> A(E.rows(), 7);
   vnl_matrix<double> B(E.rows(), 7);
 
   vnl_vector<double> s0(E.rows());
   vnl_vector<double> a0(E.rows());
   vnl_vector<double> b0(E.rows());
   vnl_vector<double> ta(E.rows());
   vnl_vector<double> tb(E.rows());
   vnl_vector<double> e(E.rows());
   vnl_vector<double> m(E.rows());
   vnl_vector<double> a(E.rows());
   vnl_vector<double> b(E.rows());
 
 
   // 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 *(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(i,0)=(bool)C(i,0) * a0(i);
     A(i,1)=(bool)C(i,1) * (1.0/3.0-(sF+2)*delta);
     A(i,2)=(bool)C(i,2) * (0.2+0.8*a0(i)-0.4*b0(i)-delta/sF);
     A(i,3)=(bool)C(i,3) * (0.2+delta/sF);
     A(i,4)=(bool)C(i,4) * (0.2*a0(i)+0.4*b0(i)+2*delta/sF);
     A(i,5)=(bool)C(i,5) * (1.0/6.0+0.5*(sF+1)*delta);
     A(i,6)=(bool)C(i,6) * a0(i);
 
     B(i,0)=(bool)C(i,0) * (1.0/3.0+delta);
     B(i,1)=(bool)C(i,1) * (1.0/3.0+delta);
     B(i,2)=(bool)C(i,2) * (0.4-0.4*a0(i)+0.2*b0(i)-2*delta/sF);
     B(i,3)=(bool)C(i,3) * (0.4-3*delta/sF);
     B(i,4)=(bool)C(i,4) * (0.4*a0(i)+0.8*b0(i)-delta/sF);
     B(i,5)=(bool)C(i,5) * (1.0/3.0+delta);
     B(i,6)=(bool)C(i,6) * b0(i);
   }
 
 
 
   for(int i = 0 ; i < E.rows(); i++)
   {
     double sumA = 0;
     double sumB = 0;
     for(int j = 0 ; j < 7; j++)
     {
       sumA += A(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]));
   }
 
 }
 
 
 
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
 ::Projection2( vnl_vector<double> & A, vnl_vector<double> & a, vnl_vector<double> & b, double delta0)
 {
 
   const double s6 = sqrt(6);
   const double s15 = s6/2.0;
 
   vnl_vector<double> delta(a.size());
   delta.fill(delta0);
 
   vnl_matrix<double> AM(a.size(), 15);
   vnl_matrix<double> aM(a.size(), 15);
   vnl_matrix<double> bM(a.size(), 15);
   vnl_matrix<unsigned char> B(a.size(), 15);
 
   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))/3.0);
   AM.set_column(5, delta);
   AM.set_column(6, delta);
   AM.set_column(7, delta);
   AM.set_column(8, A);
   AM.set_column(9, 0.2*(a*2+A-2*(s6+1)*delta));
   AM.set_column(10,0.2*(b*(-2)+A+2-2*(s6+1)*delta));
   AM.set_column(11, delta);
   AM.set_column(12, delta);
   AM.set_column(13, delta);
   AM.set_column(14, 0.5-(1+s15)*delta);
 
 
   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+s6*delta)/6.0);
   aM.set_column(5, a);
   aM.set_column(6, -delta + 1);
   aM.set_column(7, 0.5*(a+b)+(1+s15)*delta);
   aM.set_column(8, -delta + 1);
   aM.set_column(9, 0.2*(a*4+A*2+(s6+1)*delta));
   aM.set_column(10, -delta + 1);
   aM.set_column(11, (s6+3)*delta);
   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-s6*delta)/6.0);
   bM.set_column(5, delta);
   bM.set_column(6, b);
   bM.set_column(7, 0.5*(a+b)-(1+s15)*delta);
   bM.set_column(8, delta);
   bM.set_column(9, delta);
   bM.set_column(10, 0.2*(b*4-A*2+1-(s6+1)*delta));
   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;
 
   vnl_matrix<double> R2(a.size(), 15);
   std::vector<unsigned int> I(a.size());
 
   for (int i=0; i<AM.rows(); i++){
     for (int j=0; j<AM.cols(); j++){
       if (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)
         R2(i,j) = (AM(i,j)-A(i))*(AM(i,j)-A(i))+ (aM(i,j)-a(i))*(aM(i,j)-a(i))+(bM(i,j)-b(i))*(bM(i,j)-b(i));
       else
         R2(i,j) = 1e20;
     }
     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;
       }
     }
     I[i] = index;
   }
 
   for (int i=0; i < A.size(); i++){
     A(i) = AM(i,(int)I[i]);
     a(i) = aM(i,(int)I[i]);
     b(i) = bM(i,(int)I[i]);
   }
 
 }
 
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
 ::S_S0Normalization( vnl_vector<double> & 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;
     vec[i] /= b0f;
   }
 
 }
 
 
 template<class TReferenceImagePixelType, class TGradientImagePixelType, class TOdfPixelType, int NOrderL, int NrOdfDirections>
 void DiffusionMultiShellQballReconstructionImageFilter<TReferenceImagePixelType, TGradientImagePixelType, TOdfPixelType,NOrderL, NrOdfDirections>
 ::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;
       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>
 ::SetGradientImage( GradientDirectionContainerType *gradientDirection
                     , const GradientImagesType *gradientImage
                     , float bvalue)
 {
   m_BValue = bvalue;
   m_GradientDirectionContainer = gradientDirection;
   m_NumberOfBaselineImages = 0;
 
 
   if(m_BValueMap.size() == 0){
     itkWarningMacro(<< "DiffusionMultiShellQballReconstructionImageFilter.cpp : no GradientIndexMapAvalible");
 
     GradientDirectionContainerType::ConstIterator gdcit;
     for( gdcit = m_GradientDirectionContainer->Begin(); gdcit != m_GradientDirectionContainer->End(); ++gdcit)
     {
       double bValueKey = int(((m_BValue * gdcit.Value().two_norm() * gdcit.Value().two_norm())+7.5)/10)*10;
       m_BValueMap[bValueKey].push_back(gdcit.Index());
     }
 
   }
   if(m_BValueMap.find(0) == m_BValueMap.end())
   {
     itkExceptionMacro(<< "DiffusionMultiShellQballReconstructionImageFilter.cpp : GradientIndxMap with no b-Zero indecies found: check input image");
   }
 
 
   m_NumberOfBaselineImages = m_BValueMap[0].size();
   m_NumberOfGradientDirections = gradientDirection->Size() - m_NumberOfBaselineImages;
   // ensure that the gradient image we received has as many components as
   // the number of gradient directions
   if( gradientImage->GetVectorLength() != m_NumberOfBaselineImages + m_NumberOfGradientDirections )
   {
     itkExceptionMacro( << m_NumberOfGradientDirections << " gradients + " << m_NumberOfBaselineImages
                        << "baselines = " << m_NumberOfGradientDirections + m_NumberOfBaselineImages
                        << " directions specified but image has " << gradientImage->GetVectorLength()
                        << " components.");
   }
 
 
   ProcessObject::SetNthInput( 0, const_cast< GradientImagesType* >(gradientImage) );
 
   std::string gradientImageClassName(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 * >( 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();
 
 }
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::BeforeThreadedGenerateData()
 {
   m_ReconstructionType = Mode_Standard1Shell;
 
   BValueMapIteraotr it = m_BValueMap.begin();
   it++;
   const int b1 = it->first;
   const int vecSize1 = it->second.size();
   IndiciesVector shell1 = it->second;
   it++;
   const int b2 = it->first;
   const int vecSize2 = it->second.size();
   IndiciesVector shell2 = it->second;
   it++;
   const int b3 = it->first;
   const int vecSize3 = it->second.size();
   IndiciesVector shell3 = it->second;
 
   if(m_BValueMap.size() == 4 ){
 
     // arithmetic progrssion
     if(b2 - b1 == b1 && b3 - b2 == b1 )
     {
       // check if Interpolation is needed
       // if shells with different numbers of directions exist
       m_Interpolation_Flag = false;
       if(vecSize1 != vecSize2 || vecSize2 != vecSize3 || vecSize1 != vecSize3)
       {
         m_Interpolation_Flag = true;
         MITK_INFO << "Shell interpolation: shells with different numbers of directions";
       }else // if each shell holds same numbers of directions, but the gradient direction differ more than one 1 degree
       {
         m_Interpolation_Flag = CheckForDifferingShellDirections();
         if(m_Interpolation_Flag) MITK_INFO << "Shell interpolation: gradient direction differ more than one 1 degree";
       }
 
-      IndiciesVector min_shell;
-      IndiciesVector max_shell;
-      int Interpolation_SHOrder = 10;
+      m_ReconstructionType = Mode_Analytical3Shells;
 
       if(m_Interpolation_Flag)
       {
+        IndiciesVector min_shell;
+        IndiciesVector max_shell;
+        int Interpolation_SHOrder = 10;
 
         //fewer directions
         if (vecSize1 <= vecSize2 )      { min_shell = shell1;}
         else                            { min_shell = shell2;}
         if (min_shell.size() > vecSize3){ min_shell = shell3;}
 
         //most directions
         if (vecSize1 >= vecSize2 )      { max_shell = shell1;}
         else                            { max_shell = shell2;}
         if (max_shell.size() < vecSize3){ max_shell = shell3;}
 
+        m_MaxDirections = max_shell.size();
+
         //SH-order determination
         while( ((Interpolation_SHOrder+1)*(Interpolation_SHOrder+2)/2) > min_shell.size() && Interpolation_SHOrder > L )
           Interpolation_SHOrder -= 2 ;
 
         MITK_INFO << "Interpolation enabeled, using SH of order : " << Interpolation_SHOrder;
 
         // create target SH-Basis
         vnl_matrix<double> * Q = new vnl_matrix<double>(3, max_shell.size());
         ComputeSphericalFromCartesian(Q, max_shell);
 
         int NumberOfCoeffs = (int)(Interpolation_SHOrder*Interpolation_SHOrder + Interpolation_SHOrder + 2.0)/2.0 + Interpolation_SHOrder;
         m_Interpolation_TARGET_SH = new vnl_matrix<double>(max_shell.size(), NumberOfCoeffs);
         ComputeSphericalHarmonicsBasis(Q, m_Interpolation_TARGET_SH, Interpolation_SHOrder);
         delete Q;
         // end creat target SH-Basis
 
         // create measured-SHBasis
         // Shell 1
         vnl_matrix<double> * tempSHBasis;
         vnl_matrix_inverse<double> * temp;
 
         Q = new vnl_matrix<double>(3, shell1.size());
         ComputeSphericalFromCartesian(Q, shell1);
 
         tempSHBasis = new vnl_matrix<double>(shell1.size(), NumberOfCoeffs);
         ComputeSphericalHarmonicsBasis(Q, tempSHBasis, Interpolation_SHOrder);
         temp = new vnl_matrix_inverse<double>((*tempSHBasis));
 
         m_Interpolation_SHT1_inv = new vnl_matrix<double>(temp->inverse());
 
         delete Q;
         delete temp;
         delete tempSHBasis;
 
         // Shell 2
         Q = new vnl_matrix<double>(3, shell2.size());
         ComputeSphericalFromCartesian(Q, shell2);
 
         tempSHBasis = new vnl_matrix<double>(shell2.size(), NumberOfCoeffs);
         ComputeSphericalHarmonicsBasis(Q, tempSHBasis, Interpolation_SHOrder);
         temp = new vnl_matrix_inverse<double>((*tempSHBasis));
 
-        m_Interpolation_SHT1_inv = new vnl_matrix<double>(temp->inverse());
+        m_Interpolation_SHT2_inv = new vnl_matrix<double>(temp->inverse());
 
         delete Q;
         delete temp;
         delete tempSHBasis;
 
         // Shell 3
         Q = new vnl_matrix<double>(3, shell3.size());
         ComputeSphericalFromCartesian(Q, shell3);
 
         tempSHBasis = new vnl_matrix<double>(shell3.size(), NumberOfCoeffs);
 
         ComputeSphericalHarmonicsBasis(Q, tempSHBasis, Interpolation_SHOrder);
 
         temp = new vnl_matrix_inverse<double>((*tempSHBasis));
 
-        m_Interpolation_SHT1_inv = new vnl_matrix<double>(temp->inverse());
+        m_Interpolation_SHT3_inv = new vnl_matrix<double>(temp->inverse());
 
         delete Q;
         delete temp;
         delete tempSHBasis;
-      }
 
-      m_ReconstructionType = Mode_Analytical3Shells;
+        ComputeReconstructionMatrix(max_shell);
+        return;
+      }
     }
   }
 
   if(m_BValueMap.size() > 2 && m_ReconstructionType != Mode_Analytical3Shells)
   {
     m_ReconstructionType = Mode_NumericalNShells;
   }
 
   // Reconstruction Matrix
-  ComputeReconstructionMatrix(shell1);
+  ComputeReconstructionMatrix(shell2);
+
+  m_scalingFactors = new vnl_vector<double>((L+1)*(L+2)/2);
+  double P=2/(16*M_PI);
+  (*m_scalingFactors)[0]=0;
+  for (int l=2; l < L; l=l+2){
+    for (int m=-l; m<l; m++){
+      double j=l*(l+1)/2+m;
+      double prod=P*pow(-1,(float)l/2);
+      double prod2=1;
+      for (int n=3; n<=l-1; n=n+2)
+        prod=prod*n;
+      for (int n=2; n<=l; n=n+2)
+        prod2=prod2*n;
+      (*m_scalingFactors)[j]=-l*(l+1)*prod/prod2;
+    }
+  }
+
 }
 
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int NumberOfThreads)
 {
   itk::TimeProbe clock;
   GenerateAveragedBZeroImage(outputRegionForThread);
 
   clock.Start();
   switch(m_ReconstructionType)
   {
   case Mode_Standard1Shell:
     StandardOneShellReconstruction(outputRegionForThread);
     break;
   case Mode_Analytical3Shells:
     AnalyticalThreeShellReconstruction(outputRegionForThread);
     break;
   case Mode_NumericalNShells:
     break;
   }
   clock.Stop();
   MITK_INFO << "Reconstruction in : " << clock.GetTotal() << " s";
 }
 
 
 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 * >(ProcessObject::GetOutput(0));
 
   // ImageRegionIterator for the output image
   ImageRegionIterator< OutputImageType > oit(outputImage, outputRegionForThread);
   oit.GoToBegin();
 
   // ImageRegionIterator for the BZero (output) image
   ImageRegionConstIterator< BZeroImageType > bzeroImageIterator(m_BZeroImage, outputRegionForThread);
   bzeroImageIterator.GoToBegin();
 
   IndiciesVector SignalIndicies = m_BValueMap[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 * >( 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();
     // ODF Vector
     OdfPixelType odf(0.0);
 
     // Create the Signal Vector
     vnl_vector<double> SignalVector(m_NumberOfGradientDirections);
     if( (bzeroImageIterator.Get() != 0) && (bzeroImageIterator.Get() >= m_Threshold) )
     {
 
       for( unsigned int i = 0; i< SignalIndicies.size(); i++ )
       {
         SignalVector[i] = static_cast<double>(b[SignalIndicies[i]]);
       }
 
       // apply threashold an generate ln(-ln(E)) signal
       // Replace SignalVector with PreNormalized SignalVector
       S_S0Normalization(SignalVector, bzeroImageIterator.Get());
       DoubleLogarithm(SignalVector);
 
       // ODF coeffs-vector
       //coeffs(m_NumberCoefficients);
 
       // approximate ODF coeffs
       vnl_vector<double>  coeffs = ( (*m_CoeffReconstructionMatrix) * SignalVector );
       coeffs[0] = 1.0/(2.0*sqrt(QBALL_ANAL_RECON_PI));
 
       odf = element_cast<double, TO>(( (*m_ODFSphericalHarmonicBasisMatrix) * coeffs )).data_block();
       odf *= (QBALL_ANAL_RECON_PI*4/NODF);
     }
     // set ODF to ODF-Image
     oit.Set( odf );
     ++oit;
     ++bzeroImageIterator;
     ++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>
 ::NumericalNShellReconstruction(const OutputImageRegionType& outputRegionForThread)
 {
 
 
   //  vnl_levenberg_marquardt LMOptimizer = new vnl_levenberg_marquardt();
 
 }
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::GenerateAveragedBZeroImage(const OutputImageRegionType& outputRegionForThread)
 {
   typedef typename GradientImagesType::PixelType         GradientVectorType;
 
   ImageRegionIterator< BZeroImageType > bzeroIterator(m_BZeroImage, outputRegionForThread);
   bzeroIterator.GoToBegin();
 
   IndiciesVector BZeroIndicies = m_BValueMap[0];
 
   typename GradientImagesType::Pointer gradientImagePointer = static_cast< GradientImagesType * >( ProcessObject::GetInput(0) );
 
   //  Const ImageRegionIterator for input gradient image
   typedef ImageRegionConstIterator< GradientImagesType > GradientIteratorType;
   GradientIteratorType git(gradientImagePointer, outputRegionForThread );
   git.GoToBegin();
 
 
   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 /= BZeroIndicies.size();
 
     bzeroIterator.Set(b0);
     ++bzeroIterator;
     ++git;
   }
 }
 
 template< class T, class TG, class TO, int L, int NODF>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::AnalyticalThreeShellReconstruction(const OutputImageRegionType& outputRegionForThread)
 {
 
   typedef typename GradientImagesType::PixelType         GradientVectorType;
 
   // Input Gradient Image and Output ODF Image
   typename OutputImageType::Pointer outputImage = static_cast< OutputImageType * >(ProcessObject::GetOutput(0));
   typename GradientImagesType::Pointer gradientImagePointer = static_cast< GradientImagesType * >( ProcessObject::GetInput(0) );
 
 
   // Define Image iterators
   ImageRegionIterator< OutputImageType > odfOutputImageIterator(outputImage, outputRegionForThread);
   ImageRegionConstIterator< BZeroImageType > bzeroImageIterator(m_BZeroImage, outputRegionForThread);
   ImageRegionConstIterator< GradientImagesType > gradientInputImageIterator(gradientImagePointer, outputRegionForThread );
 
   // All iterators seht to Begin of the specific OutputRegion
   odfOutputImageIterator.GoToBegin();
   bzeroImageIterator.GoToBegin();
   gradientInputImageIterator.GoToBegin();
 
   // Get Shell Indicies for all non-BZero Gradients
   // it MUST be a arithmetic progression eg.: 1000, 2000, 3000
   BValueMapIteraotr it = m_BValueMap.begin();
   it++;
   // it = b-value = 1000
   IndiciesVector Shell1Indiecies = it->second;
-  int vecSize1 = it->second.size();
   it++;
   // it = b-value = 2000
   IndiciesVector Shell2Indiecies = it->second;
-  int vecSize2 = it->second.size();
   it++;
   // it = b-value = 3000
   IndiciesVector Shell3Indiecies = it->second;
-  int vecSize3 = it->second.size();
+
+
+  if(!m_Interpolation_Flag)
+  {
+    m_MaxDirections = Shell1Indiecies.size();
+  }
 
 
   // Nx3 Signal Matrix with E(0) = Shell 1, E(1) = Shell 2, E(2) = Shell 3
-  vnl_matrix< double > * E = new vnl_matrix<double>(Shell1Indiecies.size(), 3);
+  vnl_matrix< double > E(m_MaxDirections, 3);
 
-  vnl_vector<double> * AlphaValues = new vnl_vector<double>(Shell1Indiecies.size());
-  vnl_vector<double> * BetaValues = new vnl_vector<double>(Shell1Indiecies.size());
-  vnl_vector<double> * LAValues = new vnl_vector<double>(Shell1Indiecies.size());
-  vnl_vector<double> * PValues = new vnl_vector<double>(Shell1Indiecies.size());
+  vnl_vector<double> AlphaValues(m_MaxDirections);
+  vnl_vector<double> BetaValues(m_MaxDirections);
+  vnl_vector<double> LAValues(m_MaxDirections);
+  vnl_vector<double> PValues(m_MaxDirections);
+
+  vnl_vector<double> DataShell1(Shell1Indiecies.size());
+  vnl_vector<double> DataShell2(Shell2Indiecies.size());
+  vnl_vector<double> DataShell3(Shell3Indiecies.size());
 
   OdfPixelType odf(0.0);
 
   // iterate overall voxels of the gradient image region
   while( ! gradientInputImageIterator.IsAtEnd() )
   {
     if( (bzeroImageIterator.Get() != 0) && (bzeroImageIterator.Get() >= m_Threshold) )
     {
       // Get the Signal-Value for each Shell at each direction (specified in the ShellIndicies Vector .. this direction corresponse to this shell...)
       GradientVectorType b = gradientInputImageIterator.Get();
       for(int i = 0 ; i < Shell1Indiecies.size(); i++)
       {
-        E->put(i,0, static_cast<double>(b[Shell1Indiecies[i]]));
-        E->put(i,1, static_cast<double>(b[Shell2Indiecies[i]]));
-        E->put(i,2, static_cast<double>(b[Shell3Indiecies[i]]));
+        DataShell1[i] = static_cast<double>(b[Shell1Indiecies[i]]);
+      }
+      for(int i = 0 ; i < Shell2Indiecies.size(); i++)
+      {
+        DataShell2[i] = static_cast<double>(b[Shell2Indiecies[i]]);
+      }
+      for(int i = 0 ; i < Shell3Indiecies.size(); i++)
+      {
+        DataShell3[i] = static_cast<double>(b[Shell3Indiecies[i]]);
       }
 
 
       // IF GRADIENT DIRECTIONS AND NUMBERS OF DIRECTIONS ARE EQUAL EACH OTHER, THAN DO NOT INTERPOLATE
 
       //Approximated-Signal by SH fit - using the specific shell directions and values
       // approximated Signal : S = SHBasis * Coeffs
       // with Coeffs : C = (B_T * B + lambda * L) ^ -1 * B_T * OS
       // OS := Original-Signal
       //E->set_column(1, (*m_SHBasisMatrix) * ((*m_SignalReonstructionMatrix) * (E->get_column(1))));
       //E->set_column(2, (*m_SHBasisMatrix) * ((*m_SignalReonstructionMatrix) * (E->get_column(2))));
 
       if(m_Interpolation_Flag)
       {
-        E->set_column(0, ((*m_Interpolation_TARGET_SH) * (*m_Interpolation_SHT1_inv) * (E->get_column(0))));
-        E->set_column(1, ((*m_Interpolation_TARGET_SH) * (*m_Interpolation_SHT1_inv) * (E->get_column(1))));
-        E->set_column(2, ((*m_Interpolation_TARGET_SH) * (*m_Interpolation_SHT1_inv) * (E->get_column(2))));
+        E.set_column(0, ((*m_Interpolation_TARGET_SH) * (*m_Interpolation_SHT1_inv) * (DataShell1)));
+        E.set_column(1, ((*m_Interpolation_TARGET_SH) * (*m_Interpolation_SHT2_inv) * (DataShell2)));
+        E.set_column(2, ((*m_Interpolation_TARGET_SH) * (*m_Interpolation_SHT3_inv) * (DataShell3)));
+      }else
+      {
+        E.set_column(0, (DataShell1));
+        E.set_column(1, (DataShell2));
+        E.set_column(2, (DataShell3));
       }
 
       // Normalize the Signal: Si/S0
-      S_S0Normalization(*E,bzeroImageIterator.Get());
+      S_S0Normalization(E,bzeroImageIterator.Get());
 
       //Implements Eq. [19] and Fig. 4.
-      Threshold(*E);
+      Threshold(E);
 
       //inqualities [31]. Taking the lograithm of th first tree inqualities
       //convert the quadratic inqualities to linear ones.
-      Projection1(*E);
+      Projection1(E);
 
       double E1, E2, E3, P2,A,B2,B,P,alpha,beta,lambda, ER1, ER2;
 
-      for( unsigned int i = 0; i< Shell1Indiecies.size(); i++ )
+      for( unsigned int i = 0; i< m_MaxDirections; i++ )
       {
 
-        E1 = E->get(i,0);
-        E2 = E->get(i,1);
-        E3 = E->get(i,2);
+        E1 = E.get(i,0);
+        E2 = E.get(i,1);
+        E3 = E.get(i,2);
 
         P2 = E2-E1*E1;
         A = (E3 -E1*E2) / ( 2* P2);
         B2 = A * A -(E1 * E3 - E2 * E2) /P2;
         B = 0;
         if(B2 > 0) B = sqrt(B2);
 
         P = 0;
         if(P2 > 0) P = sqrt(P2);
 
         alpha = A + B;
         beta = A - B;
 
 
         lambda = 0.5 + 0.5 * std::sqrt(1 - std::pow((P * 2 ) / (alpha - beta), 2));;
         ER1 = std::fabs(lambda * (alpha - beta) + (beta - E1 ))
             + std::fabs(lambda * (std::pow(alpha, 2) - std::pow(beta, 2)) + (std::pow(beta, 2) - E2 ))
             + std::fabs(lambda * (std::pow(alpha, 3) - std::pow(beta, 3)) + (std::pow(beta, 3) - E3 ));
         ER2 = std::fabs((lambda-1) * (alpha - beta) + (beta - E1 ))
             + std::fabs((lambda-1) * (std::pow(alpha, 2) - std::pow(beta, 2)) + (std::pow(beta, 2) - E2 ))
             + std::fabs((lambda-1) * (std::pow(alpha, 3) - std::pow(beta, 3)) + (std::pow(beta, 3) - E3 ));
 
 
-        PValues->put(i, P);
-        AlphaValues->put(i, alpha);
-        BetaValues->put(i, beta);
-        LAValues->put(i,(lambda * (ER1 < ER2)) + ((1-lambda) * (ER1 >= ER2)));
+        PValues.put(i, P);
+        AlphaValues.put(i, alpha);
+        BetaValues.put(i, beta);
+        LAValues.put(i,(lambda * (ER1 < ER2)) + ((1-lambda) * (ER1 >= ER2)));
 
       }
-      Projection2(*PValues, *AlphaValues, *BetaValues);
+      Projection2(PValues, AlphaValues, BetaValues);
 
       //Threshold(*AlphaValues);
       //Threshold(*BetaValues);
 
-      DoubleLogarithm(*AlphaValues);
-      DoubleLogarithm(*BetaValues);
+      DoubleLogarithm(AlphaValues);
+      DoubleLogarithm(BetaValues);
 
-      vnl_vector<double> SignalVector(element_product((*LAValues) , (*AlphaValues)-(*BetaValues)) + (*BetaValues));
+      vnl_vector<double> SignalVector(element_product((LAValues) , (AlphaValues)-(BetaValues)) + (BetaValues));
       vnl_vector<double> coeffs((*m_CoeffReconstructionMatrix) *SignalVector );
+      //coeffs = element_product(*m_scalingFactors , coeffs);
 
       // the first coeff is a fix value
       coeffs[0] = 1.0/(2.0*sqrt(QBALL_ANAL_RECON_PI));
 
       // Cast the Signal-Type from double to float for the ODF-Image
       odf = element_cast<double, TO>( (*m_ODFSphericalHarmonicBasisMatrix) * coeffs ).data_block();
       odf *= (QBALL_ANAL_RECON_PI*4/NODF);
       //Normalize(odf);
     }
 
     // set ODF to ODF-Image
     odfOutputImageIterator.Set( odf );
     ++odfOutputImageIterator;
     // iterate
     ++bzeroImageIterator;
     ++gradientInputImageIterator;
   }
-  delete E;
-  delete AlphaValues;
-  delete BetaValues;
-  delete PValues;
-  delete LAValues;
+
 }
 
 
 
 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, int LOrder , 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 <= LOrder; k += 2)
     {
       for(int m =- k; m <= k; m++)
       {
         int j = ( k * k + k + 2 ) / 2 + m - 1;
 
         // Compute SHBasisFunctions
         if(QBallReference){
           double phi = (*QBallReference)(0,i);
           double th = (*QBallReference)(1,i);
           (*SHBasisOutput)(i,j) = mitk::sh::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 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 = m_GradientDirectionContainer->Begin(); gdcit1 != 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(IndiciesVector const & refVector)
 {
 
   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 = refVector.size();
 
   if( numberOfGradientDirections < (((L+1)*(L+2))/2) || numberOfGradientDirections < 6  )
   {
     itkExceptionMacro( << "At least (L+1)(L+2)/2 gradient directions for each shell are required; current : " << numberOfGradientDirections );
   }
 
 
   CheckDuplicateDiffusionGradients();
 
 
   const int LOrder = L;
   int NumberOfCoeffs = (int)(LOrder*LOrder + LOrder + 2.0)/2.0 + LOrder;
   MITK_INFO << NumberOfCoeffs;
   MatrixDoublePtr SHBasisMatrix(new vnl_matrix<double>(numberOfGradientDirections,NumberOfCoeffs));
   SHBasisMatrix->fill(0.0);
   VectorIntPtr SHOrderAssociation(new vnl_vector<int>(NumberOfCoeffs));
   SHOrderAssociation->fill(0.0);
   MatrixDoublePtr LaplacianBaltrami(new vnl_matrix<double>(NumberOfCoeffs,NumberOfCoeffs));
   LaplacianBaltrami->fill(0.0);
   MatrixDoublePtr FRTMatrix(new vnl_matrix<double>(NumberOfCoeffs,NumberOfCoeffs));
   FRTMatrix->fill(0.0);
   MatrixDoublePtr SHEigenvalues(new vnl_matrix<double>(NumberOfCoeffs,NumberOfCoeffs));
   SHEigenvalues->fill(0.0);
 
   MatrixDoublePtr Q(new vnl_matrix<double>(3, numberOfGradientDirections));
 
   // Convert Cartesian to Spherical Coordinates refVector -> Q
   ComputeSphericalFromCartesian(Q.get(), refVector);
 
   // SHBasis-Matrix + LaplacianBaltrami-Matrix + SHOrderAssociationVector
   ComputeSphericalHarmonicsBasis(Q.get() ,SHBasisMatrix.get() , LOrder , LaplacianBaltrami.get(), SHOrderAssociation.get(), SHEigenvalues.get());
 
   // Compute FunkRadon Transformation Matrix Associated to SHBasis Order lj
 
   for(int i=0; i<NumberOfCoeffs; i++)
   {
     (*FRTMatrix)(i,i) = 2.0 * M_PI * mitk::sh::legendre0((*SHOrderAssociation)[i]);
   }
   //ComputeFunkRadonTransformationMatrix(SHOrderAssociation.get() ,FRTMatrix.get());
 
   MatrixDoublePtr temp(new vnl_matrix<double>(((SHBasisMatrix->transpose()) * (*SHBasisMatrix)) + (m_Lambda  * (*LaplacianBaltrami))));
 
   InverseMatrixDoublePtr pseudo_inv(new vnl_matrix_inverse<double>((*temp)));
   MatrixDoublePtr inverse(new vnl_matrix<double>(NumberOfCoeffs,NumberOfCoeffs));
   (*inverse) = pseudo_inv->inverse();
 
   // ODF Factor ( missing 1/4PI ?? )
   //const double factor = /*(1/4*QBALL_ANAL_RECON_PI) +*/ (1.0/(16.0*QBALL_ANAL_RECON_PI*QBALL_ANAL_RECON_PI));
   const double factor = /*(1/4*QBALL_ANAL_RECON_PI) +*/ (2.0/(16.0*QBALL_ANAL_RECON_PI*QBALL_ANAL_RECON_PI));
   MatrixDoublePtr SignalReonstructionMatrix (new vnl_matrix<double>((*inverse) * (SHBasisMatrix->transpose())));
 
   m_CoeffReconstructionMatrix = new vnl_matrix<double>(( factor * ((*FRTMatrix) * ((*SHEigenvalues) * (*SignalReonstructionMatrix))) ));
 
 
   // SH Basis for ODF-reconstruction
   vnl_matrix_fixed<double, 3, NOdfDirections>* U = itk::PointShell<NOdfDirections, vnl_matrix_fixed<double, 3, NOdfDirections> >::DistributePointShell();
   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::sh::Cart2Sph(x,y,z,cart);
     (*U)(0,i) = cart[0];
     (*U)(1,i) = cart[1];
     (*U)(2,i) = cart[2];
   }
 
   MatrixDoublePtr tempPtr (new vnl_matrix<double>( U->as_matrix() ));
   m_ODFSphericalHarmonicBasisMatrix  = new vnl_matrix<double>(NOdfDirections,NumberOfCoeffs);
   ComputeSphericalHarmonicsBasis(tempPtr.get(), m_ODFSphericalHarmonicBasisMatrix, LOrder);
 }
 
 template< class T, class TG, class TO, int L, int NOdfDirections>
 void DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NOdfDirections>
 ::ComputeSphericalFromCartesian(vnl_matrix<double> * Q,  IndiciesVector const & refShell)
 {
   for(int i = 0; i < refShell.size(); i++)
   {
     double x = m_GradientDirectionContainer->ElementAt(refShell[i]).get(0);
     double y = m_GradientDirectionContainer->ElementAt(refShell[i]).get(1);
     double z = m_GradientDirectionContainer->ElementAt(refShell[i]).get(2);
     double cart[3];
     mitk::sh::Cart2Sph(x,y,z,cart);
     (*Q)(0,i) = cart[0];
     (*Q)(1,i) = cart[1];
     (*Q)(2,i) = cart[2];
   }
 }
 
 
 template< class T, class TG, class TO, int L, int NODF>
 bool DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::CheckDuplicateDiffusionGradients()
 {
   bool value = false;
 
   BValueMapIteraotr mapIterator = m_BValueMap.begin();
   mapIterator++;
   while(mapIterator != m_BValueMap.end())
   {
     std::vector<unsigned int>::const_iterator it1 = mapIterator->second.begin();
     std::vector<unsigned int>::const_iterator it2 = mapIterator->second.begin();
 
     for(; it1 != mapIterator->second.end(); ++it1)
     {
       for(; it2 != mapIterator->second.end(); ++it2)
       {
         if(m_GradientDirectionContainer->ElementAt(*it1) == m_GradientDirectionContainer->ElementAt(*it2) && it1 != it2)
         {
           itkWarningMacro( << "Some of the Diffusion Gradients equal each other. Corresponding image data should be averaged before calling this filter." );
           value = true;
         }
       }
     }
     ++mapIterator;
   }
   return value;
 }
 
 
 // corresponding directions between shells (e.g. dir1_shell1 vs dir1_shell2) differ more than 1 degree.
 template< class T, class TG, class TO, int L, int NODF>
 bool DiffusionMultiShellQballReconstructionImageFilter<T,TG,TO,L,NODF>
 ::CheckForDifferingShellDirections()
 {
   bool interp_flag = false;
 
   BValueMapIteraotr mapIterator = m_BValueMap.begin();
   mapIterator++;
   std::vector<unsigned int> shell1 = mapIterator->second;
   mapIterator++;
   std::vector<unsigned int> shell2 = mapIterator->second;
   mapIterator++;
   std::vector<unsigned int> shell3 = mapIterator->second;
 
   for (int i=0; i< shell1.size(); i++)
     if (fabs(dot(m_GradientDirectionContainer->ElementAt(shell1[i]), m_GradientDirectionContainer->ElementAt(shell2[i])))  <= 0.9998) {interp_flag=true; break;}
   for (int i=0; i< shell1.size(); i++)
     if (fabs(dot(m_GradientDirectionContainer->ElementAt(shell1[i]), m_GradientDirectionContainer->ElementAt(shell3[i])))  <= 0.9998) {interp_flag=true; break;}
   for (int i=0; i< shell1.size(); i++)
     if (fabs(dot(m_GradientDirectionContainer->ElementAt(shell2[i]), m_GradientDirectionContainer->ElementAt(shell3[i])))  <= 0.9998) {interp_flag=true; break;}
   return interp_flag;
 }
 
 
 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 bfba820d69..270375d79f 100644
--- a/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.h
+++ b/Modules/DiffusionImaging/Reconstruction/itkDiffusionMultiShellQballReconstructionImageFilter.h
@@ -1,240 +1,243 @@
 /*===================================================================
 
 The Medical Imaging Interaction Toolkit (MITK)
 
 Copyright (c) German Cancer Research Center,
 Division of Medical and Biological Informatics.
 All rights reserved.
 
 This software is distributed WITHOUT ANY WARRANTY; without
 even the implied warranty of MERCHANTABILITY or FITNESS FOR
 A PARTICULAR PURPOSE.
 
 See LICENSE.txt or http://www.mitk.org for details.
 
 ===================================================================*/
 
 #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> > BValueMap;
     typedef std::map<double, std::vector<unsigned int> >::iterator BValueMapIteraotr;
     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 , float bvalue);//, std::vector<bool> listOfUserSelctedBValues );
 
 
     /** 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 );
     }
 
     void Normalize(OdfPixelType & odf );
 
     void S_S0Normalization( vnl_vector<double> & vec, 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 Threshold(vnl_vector<double> & vec, double delta = 0.01);
     void Threshold(vnl_matrix<double> & mat, double delta = 0.01);
     double CalculateThreashold(const double value, const double delta);
 
     void Projection1( vnl_matrix<double> & mat, double delta = 0.01);
     void Projection2( vnl_vector<double> & A, vnl_vector<double> & alpha, vnl_vector<double> & beta, double delta = 0.01);
 
     /** 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( Lambda, double );
     itkGetMacro( Lambda, double );
 
     itkGetConstReferenceMacro( BValue, TOdfPixelType);
 
     void SetBValueMap(BValueMap map){this->m_BValueMap = map;}
 
 
 protected:
     DiffusionMultiShellQballReconstructionImageFilter();
-    ~DiffusionMultiShellQballReconstructionImageFilter() { delete m_ODFSphericalHarmonicBasisMatrix; delete m_CoeffReconstructionMatrix; };
+    ~DiffusionMultiShellQballReconstructionImageFilter() { };
     void PrintSelf(std::ostream& os, Indent indent) const;
 
     void ComputeReconstructionMatrix(IndiciesVector const & refVector);
     void ComputeODFSHBasis();
     bool CheckDuplicateDiffusionGradients();
     bool CheckForDifferingShellDirections();
     void ComputeSphericalHarmonicsBasis(vnl_matrix<double>* QBallReference, vnl_matrix<double>* SHBasisOutput, int Lorder , vnl_matrix<double>* LaplaciaBaltramiOutput =0 , vnl_vector<int>* SHOrderAssociation =0 , vnl_matrix<double> * SHEigenvalues =0);
     //void ComputeFunkRadonTransformationMatrix(vnl_vector<int>* SHOrderAssociationReference, vnl_matrix<double>* FRTMatrixOutput );
     bool CheckHemisphericalArrangementOfGradientDirections();
 
     void BeforeThreadedGenerateData();
     void ThreadedGenerateData( const OutputImageRegionType &outputRegionForThread, int NumberOfThreads );
 
     void StandardOneShellReconstruction(const OutputImageRegionType& outputRegionForThread);
     void AnalyticalThreeShellReconstruction(const OutputImageRegionType& outputRegionForThread);
     void NumericalNShellReconstruction(const OutputImageRegionType& outputRegionForThread);
     void GenerateAveragedBZeroImage(const OutputImageRegionType& outputRegionForThread);
 
 private:
 
     enum ReconstructionType
     {
         Mode_Analytical3Shells,
         Mode_NumericalNShells,
         Mode_Standard1Shell
     };
 
 
     // Interpolation
     bool m_Interpolation_Flag;
     CoefficientMatrixType m_Interpolation_SHT1_inv;
     CoefficientMatrixType m_Interpolation_SHT2_inv;
     CoefficientMatrixType m_Interpolation_SHT3_inv;
     CoefficientMatrixType m_Interpolation_TARGET_SH;
+    int m_MaxDirections;
 
     //CoefficientMatrixType m_ReconstructionMatrix;
     CoefficientMatrixType m_CoeffReconstructionMatrix;
     CoefficientMatrixType m_ODFSphericalHarmonicBasisMatrix;
     //CoefficientMatrixType m_SignalReonstructionMatrix;
     //CoefficientMatrixType m_SHBasisMatrix;
 
+    vnl_vector<double> * m_scalingFactors;
+
     /** 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 */
     float m_BValue;
 
     typename BZeroImageType::Pointer m_BZeroImage;
 
     BValueMap m_BValueMap;
 
     double m_Lambda;
 
     bool m_IsHemisphericalArrangementOfGradientDirections;
 
     bool m_IsArithmeticProgession;
 
     //int m_NumberCoefficients;
 
     ReconstructionType m_ReconstructionType;
 
 
     //------------------------- VNL-function ------------------------------------
 
     template<typename CurrentValue, typename WntValue>
     vnl_vector< WntValue> element_cast (vnl_vector< CurrentValue> const& v1)
     {
       vnl_vector<WntValue> result(v1.size());
 
       for(int i = 0 ; i < v1.size(); i++)
            result[i] = static_cast< WntValue>(v1[i]);
 
       return result;
     }
 
     template<typename type>
     double dot (vnl_vector_fixed< type ,3> const& v1, vnl_vector_fixed< type ,3 > const& v2 )
     {
       double result = (v1[0] * v2[0] + v1[1] * v2[1] + v1[2] * v2[2]) / (v1.two_norm() * v2.two_norm());
       return result ;
     }
 
     void ComputeSphericalFromCartesian(vnl_matrix<double> * Q, const IndiciesVector & refShell);
 
 };
 
 }
 
 #ifndef ITK_MANUAL_INSTANTIATION
 #include "itkDiffusionMultiShellQballReconstructionImageFilter.cpp"
 #endif
 
 #endif //__itkDiffusionMultiShellQballReconstructionImageFilter_h_