diff --git a/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx b/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx
index 509744e54f..bf2ca60813 100644
--- a/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx
+++ b/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx
@@ -1,1012 +1,1006 @@
 /*===================================================================
 
 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 _itk_TensorReconstructionWithEigenvalueCorrectionFilter_txx_
 #define _itk_TensorReconstructionWithEigenvalueCorrectioFiltern_txx_
 #endif
 #include "itkImageRegionConstIterator.h"
 #include <math.h>
 #include <mitkImageWriter.h>
 #include "itkImageFileWriter.h"
 #include "itkImage.h"
 #include "itkImageRegionIterator.h"
 
 
 
 namespace itk
 {
 
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
     ::TensorReconstructionWithEigenvalueCorrectionFilter()
   {
     m_B0Threshold = 50.0;
   }
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   void
   TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
   ::GenerateData ()
   {
 
+    // GradientImagesType input
     m_GradientImagePointer = static_cast< GradientImagesType * >(
       this->ProcessObject::GetInput(0) );
 
     typename GradientImagesType::SizeType size = m_GradientImagePointer->GetLargestPossibleRegion().GetSize();
-    int nof = m_GradientDirectionContainer->Size();
-    int numberb0=0;
 
-    int cnt=0;
+    // number of volumes
+    int nof = m_GradientDirectionContainer->Size();
 
+    // determine the number of b-zero values
+    int numberb0=0;
     for(int i=0; i<nof; i++)
     {
       vnl_vector_fixed <double, 3 > vec = m_GradientDirectionContainer->ElementAt(i);
 
+      // due to roundings, the values are not always exactly zero
       if(vec[0]<0.0001 && vec[1]<0.0001 && vec[2]<0.0001 && vec[0]>-0.0001&& vec[1]>-0.0001 && vec[2]>-0.0001)
       {
         numberb0++;
       }
     }
 
 
-    itk::Vector<double,3u> spacing_term = m_GradientImagePointer->GetSpacing();
-    itk::Matrix<double,3u,3u> direction_term = m_GradientImagePointer->GetDirection();
+    typename GradientImagesType::SpacingType spacing_term = m_GradientImagePointer->GetSpacing();
+    typename GradientImagesType::DirectionType direction_term = m_GradientImagePointer->GetDirection();
+
 
+    /*
     vnl_vector <double> spacing_vnl(3);
     vnl_matrix <double> dir_vnl (3,3);
 
     for (int i=0;i<3;i++)
     {
       spacing_vnl[i]=spacing_term[i];
 
       for(int j=0;j<3;j++)
       {
         dir_vnl[i][j]=direction_term[i][j];
       }
-    }
+    }*/
 
     vnl_matrix <double> vox_dim_step (3,3);
 
     for (int i=0;i<3;i++)
     {
       for(int j=0;j<3;j++)
       {
-        vox_dim_step[i][j]=spacing_vnl[i]*dir_vnl[i][j];
+        vox_dim_step[i][j]=spacing_term[i]*direction_term[i][j];
       }
     }
 
     vnl_symmetric_eigensystem <double> eigen_spacing(vox_dim_step);
 
     vnl_vector <double> vox_dim (3);
 
     vox_dim[0]=eigen_spacing.get_eigenvalue(0);
     vox_dim[1]=eigen_spacing.get_eigenvalue(1);
     vox_dim[2]=eigen_spacing.get_eigenvalue(2);
 
     vox_dim=vox_dim/(vox_dim.min_value());
 
 
     vnl_matrix<double> directions(nof-numberb0,3);
     m_B0Mask.set_size(nof);
 
 
 
-
+    int cnt=0;
     for(int i=0; i<nof; i++)
     {
       vnl_vector_fixed <double, 3 > vec = m_GradientDirectionContainer->ElementAt(i);
 
       if(vec[0]<0.0001 && vec[1]<0.0001 && vec[2]<0.0001 && vec[0]>-0.001&& vec[1]>-0.001 && vec[2]>-0.001)
       {
         m_B0Mask[i]=1;
       }
       else
       {
         m_B0Mask[i]=0;
         directions[cnt][0] = vec[0];
         directions[cnt][1] = vec[1];
         directions[cnt][2] = vec[2];
         cnt++;
       }
     }
 
     vnl_matrix<double> dirsTimesDirsTrans = directions*directions.transpose();
 
     vnl_vector< double> diagonal(nof-numberb0);
     vnl_vector< double> b_vec(nof-numberb0);
     vnl_vector< double> temporary(3);
 
     for (int i=0;i<nof-numberb0;i++)
     {
       diagonal[i]=dirsTimesDirsTrans[i][i];
     }
 
     double max_diagonal = diagonal.max_value();
 
 
     directions=directions*sqrt(m_BValue/1000.0)*(1.0/sqrt(max_diagonal));
 
 
     dirsTimesDirsTrans = directions*directions.transpose();
 
     for (int i=0;i<nof-numberb0;i++)
     {
       b_vec[i]= dirsTimesDirsTrans[i][i];
     }
 
     vnl_matrix<double> H(nof-numberb0, 6);
     vnl_matrix<double> H_org(nof-numberb0, 6);
     vnl_vector<double> pre_tensor(9);
 
     int etbt[6] = { 0, 4, 8, 1, 5, 2 };
 
     for (int i = 0; i < nof-numberb0; i++)
     {
       for (int j = 0; j < 3; j++)
       {
         temporary[j] = -directions[i][j];
       }
       for (int j = 0; j < 3; j++)
       {
        for (int k = 0; k < 3; k++)
        {
          pre_tensor[k + 3 * j] = temporary[k] * directions[i][j];
        }
       }
       for (int j = 0; j < 6; j++)
       {
         H[i][j] = pre_tensor[etbt[j]];
       }
       for (int j = 0; j < 3; j++)
       {
         H[i][3 + j] *= 2.0;
       }
     }
 
     H_org=H;
 
     vnl_matrix<double> inputtopseudoinverse=H.transpose()*H;
     vnl_symmetric_eigensystem<double> eig( inputtopseudoinverse);
     vnl_matrix<double> pseudoInverse = eig.pinverse()*H.transpose();
 
 
     itk::Index<3> ix;
 
     ImageType::Pointer corrected_diffusion = ImageType::New();
     corrected_diffusion->SetRegions(size);
     corrected_diffusion->SetSpacing(m_GradientImagePointer->GetSpacing());
     corrected_diffusion->SetOrigin(m_GradientImagePointer->GetOrigin());
     corrected_diffusion->SetVectorLength(nof);
     corrected_diffusion->Allocate();
 
 
     ImageType::Pointer corrected_diffusion_temp = ImageType::New();
 
     typedef itk::VariableLengthVector<short> VariableVectorType;
     VariableVectorType variableLengthVector;
     variableLengthVector.SetSize(nof);
 
 
     typedef itk::VariableLengthVector<short> VariableVectorType;
     VariableVectorType corrected_single;
     corrected_single.SetSize(nof-1);
 
 
     typedef itk::Image<short, 3> MaskImageType;
     MaskImageType::Pointer mask = MaskImageType::New();
     mask->SetRegions(m_GradientImagePointer->GetLargestPossibleRegion().GetSize());
     mask->SetSpacing(m_GradientImagePointer->GetSpacing());
     mask->SetOrigin(m_GradientImagePointer->GetOrigin());
     mask->Allocate();
 
 
     double mean_b=0.0;
 
 
     int mask_cnt=0;
     for(int x=0;x<size[0];x++)
     {
       for(int y=0;y<size[1];y++)
       {
         for(int z=0;z<size[2];z++)
         {
           mean_b=0.0;
           ix[0] = x;
           ix[1] = y;
           ix[2] = z;
 
           GradientVectorType pixel = m_GradientImagePointer->GetPixel(ix);
           for (int i=0;i<nof;i++)
           {
             if(m_B0Mask[i]==1)
             {
               mean_b = mean_b + pixel[i];
             }
           }
           mean_b=mean_b/numberb0;
           if(mean_b > m_B0Threshold)
           {
             mask->SetPixel(ix, 1);
             mask_cnt++;
           }
           else
           {
             mask->SetPixel(ix, 0);
           }
 
         }
       }
     }
 
     for ( int x=0;x<size[0];x++)
     {
       for ( int y=0;y<size[1];y++)
       {
         for ( int z=0;z<size[2];z++)
         {
           ix[0] = x; ix[1] = y; ix[2] = z;
 
           GradientVectorType pixel2 = m_GradientImagePointer->GetPixel(ix);
 
           corrected_diffusion->SetPixel(ix,pixel2);
 
         }
       }
     }
 
 
 
    //Sometimes the gradient voxels may contain negative values ( even if B0 voxel is > = 50 ). This must be corrected
 
 
     double mask_val=0.0;
         vnl_vector<double> org_vec(nof-numberb0);
 
         int counter_corrected =0;
 
         for ( int x=0;x<size[0];x++)
         {
           for ( int y=0;y<size[1];y++)
           {
             for ( int z=0;z<size[2];z++)
             {
               ix[0] = x; ix[1] = y; ix[2] = z;
 
               mask_val = mask->GetPixel(ix);
               GradientVectorType pixel2 = corrected_diffusion->GetPixel(ix);
 
               for (int i=0;i<nof;i++)
               {
                 org_vec[i]=pixel2[i];
               }
 
               if(mask_val >0)// we are dooing this only if the voxels are in the mask
               {
 
               for( int f=0;f<nof;f++)
               {
                 if(org_vec[f] <= 0) // if in one of the gradients it is 0 or les than 0
                 {
                   org_vec[f] = CheckNeighbours(x,y,z,f,size,mask,corrected_diffusion);
                   counter_corrected++;
 
                   // If method returned 0 it means that there is no valid ( correct) neighborhood. It is done outside the function because
                   //later the same method is called in different concept when this is not important
                 }
               }
 
               for (int i=0;i<nof;i++)
               {
                 pixel2[i]=org_vec[i];
               }
 
               corrected_diffusion->SetPixel(ix, pixel2);
 
 
               }
             }
           }
         }
 
 
 
     typedef itk::Image< itk::DiffusionTensor3D<float>, 3 > TensorImageType;
     TensorImageType::Pointer tensorImg = TensorImageType::New();
     tensorImg->SetRegions(m_GradientImagePointer->GetLargestPossibleRegion().GetSize());
     tensorImg->SetSpacing(m_GradientImagePointer->GetSpacing());
     tensorImg->SetOrigin(m_GradientImagePointer->GetOrigin());
     tensorImg->Allocate();
 
 
     TensorImageType::Pointer temp_tensorImg = TensorImageType::New();
 
 
     DeepCopyTensorImage(tensorImg,temp_tensorImg);
 
     vnl_vector< double> pixel_max(nof-numberb0);
     vnl_vector< double> pixel_min(nof-numberb0);
 
     for (int i=0;i<nof-numberb0;i++)
     {
       pixel_max[i]=exp(-b_vec[i]*0.01);// here some values for low and high diffusivity were pre define as 0.01 for low and 5 for high
       pixel_min[i]= exp(-b_vec[i]*5);
     }
 
     m_PseudoInverse = pseudoInverse;
     m_H = H_org;
     m_BVec=b_vec;
 
 
 
     double what_mask=0;
     double set_mask=2.0;
     double previous_mask=0;
 
     double old_number_negative_eigs=0;
     double new_number_negative_eigs=0;
 
 
     bool  stil_correcting = true;
 
     TurnMask(size,mask,previous_mask,set_mask);// simply defining all possible tensors as with negative eigenvalues
 
     what_mask=1.0;// we want to calculate all the tensors
 
 
     DeepCopyDiffusionImage(corrected_diffusion,corrected_diffusion_temp,nof);
 
     GenerateTensorImage(nof,numberb0,size,corrected_diffusion_temp,mask,what_mask,tensorImg);// generating of the tensors
 
 
     old_number_negative_eigs = CheckNegatives (size,mask,tensorImg);// checking how many tensors has problems, this is working only for mask =2
 
     std::cout << "Number of negative eigenvalues: " << old_number_negative_eigs << std::endl;// info for Thomas: Debug stuff - to be removed
 
     CorrectDiffusionImage(nof,numberb0,size,corrected_diffusion_temp,corrected_diffusion_temp,mask,pixel_max,pixel_min);
 
 
 
 
     while (stil_correcting == true)
     {
 
     std::cout << "Number of negative eigenvalues: " << old_number_negative_eigs << std::endl;// info for Thomas: Debug stuff - to be removed
 
     GenerateTensorImage(nof,numberb0,size,corrected_diffusion_temp,mask,what_mask,tensorImg);
 
     new_number_negative_eigs = CheckNegatives (size,mask,tensorImg);
 
     if(new_number_negative_eigs<old_number_negative_eigs)
     {
         stil_correcting=true;
         old_number_negative_eigs=new_number_negative_eigs;
         DeepCopyDiffusionImage(corrected_diffusion_temp,corrected_diffusion,nof);
 
 
     }
     else
     {
         stil_correcting=false;
     }
 
 
     CorrectDiffusionImage(nof,numberb0,size,corrected_diffusion_temp,corrected_diffusion_temp,mask,pixel_max,pixel_min);
 
 
     }//end of while
 
 
     TurnMask(size, mask,1,1);
 
     GenerateTensorImage(nof,numberb0,size,corrected_diffusion,mask,what_mask,tensorImg);
 
     m_MaskImage = mask;
 
     this->SetNthOutput(0, tensorImg);
 
     m_VectorImage = corrected_diffusion;
 
     m_Voxdim = vox_dim;
 
 
   }
 
 
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   void
     TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
     ::SetGradientImage( GradientDirectionContainerType *gradientDirection,
     const GradientImagesType *gradientImage )
   {
     // Make sure crazy users did not call both AddGradientImage and
     // SetGradientImage
     if( m_GradientImageTypeEnumeration == GradientIsInManyImages )
     {
       itkExceptionMacro( << "Cannot call both methods:"
         << "AddGradientImage and SetGradientImage. Please call only one of them.");
     }
 
     this->m_GradientDirectionContainer = gradientDirection;
 
 
     unsigned int numImages = gradientDirection->Size();
     this->m_NumberOfBaselineImages = 0;
 
     this->m_NumberOfGradientDirections = numImages - 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 + this->m_NumberOfGradientDirections )
     {
       itkExceptionMacro( << this->m_NumberOfGradientDirections << " gradients + " << this->m_NumberOfBaselineImages
         << "baselines = " << this->m_NumberOfGradientDirections + this->m_NumberOfBaselineImages
         << " directions specified but image has " << gradientImage->GetVectorLength()
         << " components.");
     }
 
     this->ProcessObject::SetNthInput( 0,
       const_cast< GradientImagesType* >(gradientImage) );
     m_GradientImageTypeEnumeration = GradientIsInASingleImage;
   }
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   double
   TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
   ::CheckNeighbours(int x, int y, int z,int f, itk::Size<3> size, itk::Image<short, 3>::Pointer mask, itk::VectorImage<short, 3>::Pointer corrected_diffusion_temp)
   {
+
+    // Definition of neighbourhood avoiding crossing the image boundaries
+    int x_max=size[0];
+    int y_max=size[1];
+    int z_max=size[2];
+
     double back_x=std::max(0,x-1);
     double back_y=std::max(0,y-1);
     double back_z=std::max(0,z-1);
 
-    int x_max=size[0];int y_max=size[1];int z_max=size[2];// converting short to int
-
     double forth_x=std::min((x+1),x_max);
     double forth_y=std::min((y+1),y_max);
-    double forth_z=std::min((z+1),z_max);// setting constraints for neigborhood
+    double forth_z=std::min((z+1),z_max);
 
 
     double tempsum=0;
     itk::Index<3> ix;
     double temp_number=0;
-    double temp_mask=0;// declaration of variables
-    double one =1.0;
+    double temp_mask=0;
 
-
-    for(int i=back_x; i<forth_x+1; i++)
+    for(int i=back_x; i<=forth_x; i++)
     {
-      for (int j=back_y; j<forth_y+1; j++)
+      for (int j=back_y; j<=forth_y; j++)
       {
-        for (int c=back_z; c<forth_z+1;c++)
+        for (int k=back_z; k<=forth_z; k++)
         {
 
-          ix[0] = i;ix[1] = j;ix[2] = c;
+          ix[0] = i;
+          ix[1] = j;
+          ix[2] = k;
 
           temp_mask=mask->GetPixel(ix);
 
           //if(temp_mask > 0 && temp_mask < 2 )
           //{
               //GradientVectorType p = m_GradientImagePointer->GetPixel(ix);
-              GradientVectorType p = corrected_diffusion_temp->GetPixel(ix);
-
-              double test= p[f];
-
-              if (test > 0.0 )// hmm this must be here becaus the method is used in multiple ocasions. Sometiems we may deal with negative values
-              {
-                if(i==x && j==y && c== z)
-                {
-
-
-
-                }
-                else
-                {
-                    tempsum=tempsum+p[f];// sum for calculation of mean
-                    temp_number++;// number for calculation of mean
-                }
-
+          GradientVectorType p = corrected_diffusion_temp->GetPixel(ix);
 
+          double test= p[f];
 
+          if (test > 0.0 )// hmm this must be here becaus the method is used in multiple ocasions. Sometiems we may deal with negative values
+          {
+            if(!(i==x && j==y && k== z))
+            {
+                tempsum=tempsum+p[f];// sum for calculation of mean
+                temp_number++;// number for calculation of mean
             }
 
-          //}//end of mask condition
-
+          }
 
 
         }
       }
     }// end of size
 
 
 
     ix[0] = x;ix[1] = y;ix[2] = z;
 
     if (temp_number <= 0.0)
     {
       //ix[0] = x;ix[1] = y;ix[2] = z;
       //GradientVectorType p = m_GradientImagePointer->GetPixel(ix);
       //tempsum=p[f];
       tempsum=0;
       mask->SetPixel(ix,0);
     }
     else
     {
       tempsum=tempsum/temp_number;
 
     }
 
 
-
-    double ret = tempsum;
-
-
-    return ret;
+    return tempsum;
   }
 
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   void
   TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
   ::CalculateAttenuation(vnl_vector<double> org_data,vnl_vector<double> &atten,int nof, int numberb0)
   {
     double mean_b=0.0;
 
     for (int i=0;i<nof;i++)
     {
       if(m_B0Mask[i]>0)
       {
         double o_d=org_data[i];
         mean_b=mean_b+org_data[i];
       }
 
     }
     mean_b=mean_b/numberb0;
     int cnt=0;
     for (int i=0;i<nof;i++)
     {
       if(m_B0Mask[i]==0)
       {
         //if(org_data[i]<0.001){org_data[i]=0.01;}
         atten[cnt]=org_data[i]/mean_b;
         cnt++;
       }
     }
   }
 
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   void
   TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
   ::CalculateTensor(vnl_vector<double> atten,vnl_vector<double> &tensor, int nof,int numberb0)
   {
     for (int i=0;i<nof-numberb0;i++)
     {
 
       if(atten[i]<=0.0)
       {
           atten[i]==0.1;
       }
      std::cout << "problem";
       atten[i]=log((double)atten[i]);
     }
     tensor = m_PseudoInverse*atten;
 
 
   }// end of void calculate tensor
 
 
   // info for Thomas ( to be removed) : start of "lots of new code"
 
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   double
   TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
   ::CheckNegatives ( itk::Size<3> size, itk::Image<short, 3>::Pointer mask, itk::Image< itk::DiffusionTensor3D<float>, 3 >::Pointer tensorImg )
   {
 
       // The method was created to simplif the flow of negative eigenvalue correction process. The method itself just return the number
       // of voxels (tensors) with negative eigenvalues. Then if the voxel was previously bad ( mask=2 ) but it is not bad anymore mask is
       //changed to 1.
 
       double badvoxels=0;
       double pixel=0;
       itk::Index<3> ix;
       itk::DiffusionTensor3D<float> ten;
       vnl_matrix<double> temp_tensor(3,3);
       vnl_vector<double> eigen_vals(3);// declaration of important structures and variables
       vnl_vector<double> tensor (6);
 
 
       for (int x=0;x<size[0];x++)
       {
 
         for (int y=0;y<size[1];y++)
         {
 
           for (int z=0;z<size[2];z++)
           {
               // for every pixel
 
               ix[0] = x; ix[1] = y; ix[2] = z;
 
               pixel = mask->GetPixel(ix);
 
               if(pixel > 1)
               {// but only if previously marked as bad or potentially bad one
 
               ten = tensorImg->GetPixel(ix);
 
               tensor[0] = ten(0,0);
               tensor[3] = ten(0,1);
               tensor[5] = ten(0,2);
               tensor[1] = ten(1,1);
               tensor[4] = ten(1,2);
               tensor[2] = ten(2,2);
 
               temp_tensor[0][0]= tensor[0]; temp_tensor[1][0]= tensor[3]; temp_tensor[2][0]= tensor[5];
               temp_tensor[0][1]= tensor[3]; temp_tensor[1][1]= tensor[1]; temp_tensor[2][1]= tensor[4];
               temp_tensor[0][2]= tensor[5]; temp_tensor[1][2]= tensor[4]; temp_tensor[2][2]= tensor[2];
 
 
               vnl_symmetric_eigensystem<double> eigen_tensor(temp_tensor);//creating an eigensystem out of 3x3 symmetric tensor matrix
 
 
               eigen_vals[0]=eigen_tensor.get_eigenvalue(0);
               eigen_vals[1]=eigen_tensor.get_eigenvalue(1);
               eigen_vals[2]=eigen_tensor.get_eigenvalue(2);// calculating eigenvalues for current tensor
 
               if( eigen_vals[0]>0.01 && eigen_vals[1]>0.01 && eigen_vals[2]>0.01)
               {
                 mask->SetPixel(ix,1);
 
               }
               else
               {
                 badvoxels++;// increasing a number of detected bad
               }
 
               }
 
           }
          }
        }
 
       double ret = badvoxels;//returning just the number of bad voxels
 
       return ret;
 
   }// end of void check negativity
 
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   void
   TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
   ::CorrectDiffusionImage(int nof,int numberb0,itk::Size<3> size,itk::VectorImage<short, 3>::Pointer corrected_diffusion_temp,itk::VectorImage<short, 3>::Pointer corrected_diffusion,itk::Image<short, 3>::Pointer mask,vnl_vector< double> pixel_max,vnl_vector< double> pixel_min)
   {
       // in this method the diffusion image temporray is corrected with use of information from updated mask. The diffusion image is tempora
       //-ry while we can't use corrected voxel to correct other voxel in the same iteration.
 
 
       itk::Index<3> ix;
       vnl_vector<double> org_data(nof-numberb0);
       vnl_vector<double> atten(nof-numberb0);
       double cnt_atten=0;// declaration of important variables
 
       for (int z=0;z<size[2];z++)
       {
 
         for (int x=0;x<size[0];x++)
         {
 
           for (int y=0;y<size[1];y++)
           {
               // for every pixel
 
               ix[0] = x; ix[1] = y; ix[2] = z;
 
 
               if(mask->GetPixel(ix) > 1.0)
               {
                   GradientVectorType  pt = corrected_diffusion->GetPixel(ix);
 
                   for (int i=0;i<nof;i++)
                   {
                     org_data[i]=pt[i];
                   }
 
                   //CalculateAttenuation(org_data,atten,nof,numberb0);
 
                   double mean_b=0.0;
 
                   for (int i=0;i<nof;i++)
                   {
                     if(m_B0Mask[i]>0)
                     {
                       mean_b=mean_b+org_data[i];
                     }
 
                    }
                   mean_b=mean_b/numberb0;
                   int cnt=0;
                   for (int i=0;i<nof;i++)
                   {
                     if(m_B0Mask[i]==0)
                     {
                       //if(org_data[i]<0.001){org_data[i]=0.01;}
                       atten[cnt]=org_data[i]/mean_b;
                       cnt++;
                     }
                   }
 
                   cnt_atten=0;
 
                   for (int f=0;f<nof;f++)
                   {
                     if(m_B0Mask[f]==0)
                     {
 
                         if(atten[cnt_atten]<pixel_min[cnt_atten] || atten[cnt_atten]> pixel_max[cnt_atten])
                     {
                         org_data[f] = CheckNeighbours(x,y,z,f,size,mask,corrected_diffusion_temp);
 
                     }
 
 
                     cnt_atten++;
 
                    }
                     if(m_B0Mask[f]==1)
                     {
 
 
                         org_data[f] = CheckNeighbours(x,y,z,f,size,mask,corrected_diffusion_temp);
 
 
                     }
 
 
                   }//end for
 
 
 
                   for (int i=0;i<nof;i++)
                   {
                     pt[i]=org_data[i];
                   }
 
                   corrected_diffusion_temp->SetPixel(ix, pt);
 
               }
               else
               {
                   GradientVectorType  pt = corrected_diffusion->GetPixel(ix);
                   corrected_diffusion_temp->SetPixel(ix, pt);
 
               }
            }
         }
        }
 
       std::cout << "break point";
 
   }
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   void
   TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
   ::GenerateTensorImage(int nof,int numberb0,itk::Size<3> size,itk::VectorImage<short, 3>::Pointer corrected_diffusion,itk::Image<short, 3>::Pointer mask,double what_mask,itk::Image< itk::DiffusionTensor3D<float>, 3 >::Pointer tensorImg)
   {
       // in this method the whole tensor image is updated with a tensors for defined voxels ( defined by a value of mask);
 
 
       itk::Index<3> ix;
       vnl_vector<double> org_data(nof-numberb0);
       vnl_vector<double> atten(nof-numberb0);
       vnl_vector<double> tensor(6);
       itk::DiffusionTensor3D<float> ten;
       double mask_val=0;
 
 
       for (int x=0;x<size[0];x++)
       {
 
         for (int y=0;y<size[1];y++)
         {
 
          for (int z=0;z<size[2];z++)
           {
               // for every pixel
 
               ix[0] = x; ix[1] = y; ix[2] = z;
 
               mask_val= mask->GetPixel(ix);
 
               if( mask_val > 0.0 )
               {
                  GradientVectorType pt = corrected_diffusion->GetPixel(ix);
 
                   for (int i=0;i<nof;i++)
                   {
                     org_data[i]=pt[i];
                   }
 
                   double mean_b=0.0;
 
                   for (int i=0;i<nof;i++)
                   {
                     if(m_B0Mask[i]>0)
                     {
                       double o_d=org_data[i];
                       mean_b=mean_b+org_data[i];
                     }
 
                   }
                   mean_b=mean_b/numberb0;
                   int cnt=0;
                   for (int i=0;i<nof;i++)
                   {
                     if (org_data[i]<= 0)
 
                     {
                         org_data[i]=0.1;
                     }
                     if(m_B0Mask[i]==0)
                     {
                       atten[cnt]=org_data[i]/mean_b;
                       cnt++;
                     }
                   }
 
 
 
 
                   for (int i=0;i<nof-numberb0;i++)
                   {
 
                     if(atten[i]==0.0)
                    std::cout << "problem";
                     atten[i]=log((double)atten[i]);
                   }
                   tensor = m_PseudoInverse*atten;
 
                   ten(0,0) = tensor[0];
                   ten(0,1) = tensor[3];
                   ten(0,2) = tensor[5];
                   ten(1,1) = tensor[1];
                   ten(1,2) = tensor[4];
                   ten(2,2) = tensor[2];
 
                   tensorImg->SetPixel(ix, ten);
 
 
 
               }
               else if (mask_val < 1.0)
               {
                   ten(0,0) = 0;
                   ten(0,1) = 0;
                   ten(0,2) = 0;
                   ten(1,1) = 0;
                   ten(1,2) = 0;
                   ten(2,2) = 0;
                   tensorImg->SetPixel(ix, ten);
               }
 
 
 
 
            }
         }
        }
 
 
 
   }// end of Generate Tensor
 
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   void
   TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
   ::TurnMask( itk::Size<3> size, itk::Image<short, 3>::Pointer mask, double previous_mask, double set_mask)
   {
       // in this method voxels in the mask that poses a certain value are substituded by other value. It is connected to the
       //idea in the algorithm. It is called more tna once so separate method spares some lines of code.
 
 
   itk::Index<3> ix;
   double temp_mask_value=0;
 
   for(int x=0;x<size[0];x++)
   {
     for(int y=0;y<size[1];y++)
     {
       for(int z=0;z<size[2];z++)
       {
           ix[0] = x; ix[1] = y; ix[2] = z;
 
           temp_mask_value=mask->GetPixel(ix);
 
           if(temp_mask_value>previous_mask)
           {
              mask->SetPixel(ix,set_mask);
           }
       }
     }
    }
 }//end of turn mas
 
 
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   void
   TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
   ::DeepCopyDiffusionImage(itk::VectorImage<short, 3>::Pointer corrected_diffusion, itk::VectorImage<short, 3>::Pointer corrected_diffusion_temp,int nof)
   {
     corrected_diffusion_temp->SetSpacing(corrected_diffusion->GetSpacing());
     corrected_diffusion_temp->SetOrigin(corrected_diffusion->GetOrigin());
     corrected_diffusion_temp->SetVectorLength(nof);
     corrected_diffusion_temp->SetRegions(corrected_diffusion->GetLargestPossibleRegion());
     corrected_diffusion_temp->Allocate();
 
     itk::ImageRegionConstIterator<ImageType> inputIterator(corrected_diffusion, corrected_diffusion->GetLargestPossibleRegion());
     itk::ImageRegionIterator<ImageType> outputIterator(corrected_diffusion_temp, corrected_diffusion_temp->GetLargestPossibleRegion());
 
     inputIterator.GoToBegin();
     outputIterator.GoToBegin();
 
     while(!inputIterator.IsAtEnd())
       {
       outputIterator.Set(inputIterator.Get());
       ++inputIterator;
       ++outputIterator;
       }
   }
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   void
   TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
   ::DeepCopyTensorImage(itk::Image< itk::DiffusionTensor3D<float>, 3 >::Pointer tensorImg, itk::Image< itk::DiffusionTensor3D<float>, 3 >::Pointer temp_tensorImg)
   {
 
       temp_tensorImg->SetSpacing(tensorImg->GetSpacing());
       temp_tensorImg->SetOrigin(tensorImg->GetOrigin());
       temp_tensorImg->SetRegions(tensorImg->GetLargestPossibleRegion());
       temp_tensorImg->Allocate();
 
     itk::ImageRegionConstIterator<TensorImageType> inputIterator(tensorImg, tensorImg->GetLargestPossibleRegion());
     itk::ImageRegionIterator<TensorImageType> outputIterator(temp_tensorImg, temp_tensorImg->GetLargestPossibleRegion());
 
     inputIterator.GoToBegin();
     outputIterator.GoToBegin();
 
     while(!inputIterator.IsAtEnd())
       {
       outputIterator.Set(inputIterator.Get());
       ++inputIterator;
       ++outputIterator;
       }
   }
 
 
 
 } // end of namespace