diff --git a/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx b/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx
index 3023cb57ca..27c34ea34e 100644
--- a/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx
+++ b/Modules/DiffusionImaging/DiffusionCore/Algorithms/itkTensorReconstructionWithEigenvalueCorrectionFilter.txx
@@ -1,974 +1,974 @@
 /*===================================================================
 
 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 "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 ()
   {
 
     m_GradientImagePointer = static_cast< GradientImagesType * >(
       this->ProcessObject::GetInput(0) );
 
     typename GradientImagesType::SizeType size = m_GradientImagePointer->GetLargestPossibleRegion().GetSize();
 
     // 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++;
       }
     }
 
 
     // Matrix to store all diffusion encoding gradients
     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)
+      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)
       {
         // the diffusion encoding gradient is approximately zero, wo we are dealing with a non-diffusion weighted volume
         m_B0Mask[i]=1;
       }
       else
       {
         // dealing with a diffusion weighted volume
         m_B0Mask[i]=0;
 
         // set the diffusion encoding gradient to the directions matrix
         directions[cnt][0] = vec[0];
         directions[cnt][1] = vec[1];
         directions[cnt][2] = vec[2];
 
         cnt++;
       }
     }
 
     // looking for maximal norm among gradients.
     // The norm is calculated with use of spectral radius theorem- based on determination of eigenvalue.
 
     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();
 
 
     // normalization: free-water method assumes that directions matrix norm 1 is equal to b=1000
 
     directions=directions*sqrt(m_BValue/1000.0)*(1.0/sqrt(max_diagonal));
 
     //calculation of norms and storing them in vector
 
     dirsTimesDirsTrans = directions*directions.transpose();
 
     for (int i=0;i<nof-numberb0;i++)
     {
       b_vec[i]= dirsTimesDirsTrans[i][i];
     }
 
     // Calculation of so called design matrix that is used to obtain expected signal.
 
     vnl_matrix<double> H(nof-numberb0, 6);
     vnl_matrix<double> H_org(nof-numberb0, 6);
     vnl_vector<double> pre_tensor(9);
 
     //H is matrix that containes covariances for directions. It is stored twice because its original value is needed later
     // while H is changed
 
     int etbt[6] = { 0, 4, 8, 1, 5, 2 };// tensor order
 
     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;
 
     // calculation of inverse matrix by means of pseudoinverse
 
     vnl_matrix<double> inputtopseudoinverse=H.transpose()*H;
     vnl_symmetric_eigensystem<double> eig( inputtopseudoinverse);
     vnl_matrix<double> pseudoInverse = eig.pinverse()*H.transpose();
 
 
 
     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();
 
     // Image thresholding: For every voxel mean B0 image is calculated and then voxels of mean B0 less than the
     // treshold on the B0 image proviced by the userare excluded from the dataset with use of defined mask image.
     // 1 in mask voxel means that B0 > assumed treshold.
 
     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++)
         {
           double mean_b=0.0;
 
           itk::Index<3> ix = {{x,y,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);
           }
 
         }
       }
     }
 
 
     //create a copy of the original image- it is then used in pre-processing methods
 
     m_CorrectedDiffusionVolumes = ImageType::New();
     m_CorrectedDiffusionVolumes->SetRegions(size);
     m_CorrectedDiffusionVolumes->SetSpacing(m_GradientImagePointer->GetSpacing());
     m_CorrectedDiffusionVolumes->SetOrigin(m_GradientImagePointer->GetOrigin());
     m_CorrectedDiffusionVolumes->SetVectorLength(nof);
     m_CorrectedDiffusionVolumes->Allocate();
 
 
     for ( int x=0;x<size[0];x++)
     {
       for ( int y=0;y<size[1];y++)
       {
         for ( int z=0;z<size[2];z++)
         {
           itk::Index<3> ix = {{x,y,z}};
 
           GradientVectorType p = m_GradientImagePointer->GetPixel(ix);
 
           m_CorrectedDiffusionVolumes->SetPixel(ix,p);
 
         }
       }
     }
 
 
 
    //Sometimes the gradient voxels may contain negative values ( even if B0 voxel is > = 50 ). This must be corrected by smoothing DWI
    //Smoothing is done by aproximation of negative voxel value by its correct ( positive) 27-th neighborhood.
 
 
     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++)
         {
           itk::Index<3> ix = {{x,y,z}};
 
           mask_val = mask->GetPixel(ix);
           GradientVectorType pixel2 = m_CorrectedDiffusionVolumes->GetPixel(ix);
 
           for (int i=0;i<nof;i++)
           {
             org_vec[i]=pixel2[i];
           }
 
           if(mask_val >0)
           {
 
             for( int f=0;f<nof;f++)
             {
               if(org_vec[f] <= 0)
               {
                 org_vec[f] = CheckNeighbours(x,y,z,f,size,mask,m_CorrectedDiffusionVolumes);
                 counter_corrected++;
 
               }
             }
 
             for (int i=0;i<nof;i++)
             {
               pixel2[i]=org_vec[i];
             }
 
             m_CorrectedDiffusionVolumes->SetPixel(ix, pixel2);
 
 
           }
         }
       }
     }
 
 
 
     typename TensorImageType::Pointer tensorImg = TensorImageType::New();
     tensorImg->SetRegions(m_GradientImagePointer->GetLargestPossibleRegion().GetSize());
     tensorImg->SetSpacing(m_GradientImagePointer->GetSpacing());
     tensorImg->SetOrigin(m_GradientImagePointer->GetOrigin());
     tensorImg->Allocate();
 
 
     typename TensorImageType::Pointer temp_tensorImg = TensorImageType::New();
 
     // Deep copy a temporary tensor image for the pre-processing methods.
 
     DeepCopyTensorImage(tensorImg,temp_tensorImg);
 
     //Declaration of vectors that contains too high or too low atenuation for each gradient. Attenuation is only calculated for
     //non B0 images so nof-numberb0.
 
     vnl_vector< double> pixel_max(nof-numberb0);
     vnl_vector< double> pixel_min(nof-numberb0);
 
     // to high and to low attenuation is calculated with use of highest allowed =5 and lowest allowed =0.01 diffusion coefficient
 
     for (int i=0;i<nof-numberb0;i++)
     {
       pixel_max[i]=exp(-b_vec[i]*0.01);
       pixel_min[i]= exp(-b_vec[i]*5);
     }
 
     m_PseudoInverse = pseudoInverse;
     m_H = H_org;
     m_BVec=b_vec;
 
     // in preprocessing we are dealing with 3 types of voxels: voxels excluded = 0 in mask, voxels correct =1 and voxels under correction
     //= 2. During pre processing most of voxels should be switched from 2 to 1.
 
 
 
 
     // what mask is a variable declared to simplify tensor calculaton. Tensors are obtained only for voxels that have a mask value bigger
     // than what_mask. Sometimes it is 1 sometimes 2.
 
     // initialization of required variables
     double what_mask=1.0;
     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
 
 
     //Preprocessing is performed in multiple iterations as long as the next iteration does not increase the number of bad voxels.
     //The final DWI should be the one that has a smaller or equal number of bad voxels as in the
     //previous iteration. To obtain this temporary DWI image must be stored in memory.
 
     DeepCopyDiffusionImage(m_CorrectedDiffusionVolumes,corrected_diffusion_temp,nof);
 
     // generating of initial tensor image
     GenerateTensorImage(nof,numberb0,size,corrected_diffusion_temp,mask,what_mask,tensorImg);
 
 
     // checking how many tensors has problems, this is working only for mask =2
     old_number_negative_eigs = CheckNegatives (size,mask,tensorImg);
 
 
     //info for the user printed in the consol-debug information to be removed in the future
     std::cout << "Number of negative eigenvalues: " << old_number_negative_eigs << std::endl;
 
 
     //Smoothing DWI - method is described when it is defined
     CorrectDiffusionImage(nof,numberb0,size,corrected_diffusion_temp,mask,pixel_max,pixel_min);
 
 
 
     while (stil_correcting == true)
     {
       //info for the user printed in the consol-debug information to be removed in the future
       std::cout << "Number of negative eigenvalues: " << old_number_negative_eigs << std::endl;
 
       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)
       {
         // if the correction does not introduce new negative eigenvalues and corrects some of negative detected n previous iteration
         // smoothed DWI is used to substitute DWI from previous iteration
         stil_correcting=true;
         old_number_negative_eigs=new_number_negative_eigs;
         DeepCopyDiffusionImage(corrected_diffusion_temp,m_CorrectedDiffusionVolumes,nof);
       }
 
       else
       {
         stil_correcting=false;
       }
 
 
       CorrectDiffusionImage(nof,numberb0,size,corrected_diffusion_temp,mask,pixel_max,pixel_min);
 
 
     }
 
     // Changing the mask value for voxels that are still not corrcted. Original idea is to take them into analysis even though
     // eigenvalues are still negative
     TurnMask(size, mask,1,1);
 
     // Generation of final pre-processed tensor image that might be used as an input for FWE method
     GenerateTensorImage(nof,numberb0,size,m_CorrectedDiffusionVolumes,mask,what_mask,tensorImg);
 
     m_MaskImage = mask;
 
     this->SetNthOutput(0, tensorImg);
 
   }
 
 
 
   template <class TDiffusionPixelType, class TTensorPixelType>
   void
     TensorReconstructionWithEigenvalueCorrectionFilter<TDiffusionPixelType, TTensorPixelType>
     ::SetGradientImage( GradientDirectionContainerType *gradientDirection,
     const GradientImagesType *gradientImage )
   {
 
     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)
   {
     // method is used for finding a new value for the voxel with use of its 27 neighborhood. To perform such a smoothing correct voxels are
     // counted an arithmetical mean is calculated and stored as a new value for the voxel. If there is no proper neigborhood voxel is turned
     // to the value of 0.
 
     // 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);
 
     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);
 
 
     double tempsum=0;
     double temp_number=0;
     double temp_mask=0;
 
     for(int i=back_x; i<=forth_x; i++)
     {
       for (int j=back_y; j<=forth_y; j++)
       {
         for (int k=back_z; k<=forth_z; k++)
         {
           itk::Index<3> ix = {i,j,k};
           temp_mask=mask->GetPixel(ix);
 
 
           GradientVectorType p = corrected_diffusion_temp->GetPixel(ix);
 
           double test= p[f];
 
           if (test > 0.0 )// taking only positive values and counting them
           {
             if(!(i==x && j==y && k== z))
             {
                 tempsum=tempsum+p[f];
                 temp_number++;
             }
 
           }
 
 
         }
       }
     }
 
     //getting back to the original position of the voxel
 
     itk::Index<3> ix = {x,y,z};
 
     if (temp_number <= 0.0)
     {
       tempsum=0;
       mask->SetPixel(ix,0);
     }
     else
     {
       tempsum=tempsum/temp_number;
 
     }
 
 
     return tempsum;// smoothed value of voxel
   }
 
 
   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>
   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.
 
       // declaration of important structures and variables
       double badvoxels=0;
       double pixel=0;
       itk::DiffusionTensor3D<float> ten;
       vnl_matrix<double> temp_tensor(3,3);
       vnl_vector<double> eigen_vals(3);
       vnl_vector<double> tensor (6);
 
       // for every pixel from the image
       for (int x=0;x<size[0];x++)
       {
 
         for (int y=0;y<size[1];y++)
         {
 
           for (int z=0;z<size[2];z++)
           {
 
 
               itk::Index<3> ix = {x,y,z};
               pixel = mask->GetPixel(ix);
 
               // but only if previously marked as bad one-negative eigen value
 
               if(pixel > 1)
               {
 
                 ten = tensorImg->GetPixel(ix);
 
                 // changing order from tensor structure in MITK into vnl structure 3x3 symmetric tensor matrix
 
                 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];
 
                 //checking negativity of tensor eigenvalues
 
                 vnl_symmetric_eigensystem<double> eigen_tensor(temp_tensor);
 
 
                 eigen_vals[0]=eigen_tensor.get_eigenvalue(0);
                 eigen_vals[1]=eigen_tensor.get_eigenvalue(1);
                 eigen_vals[2]=eigen_tensor.get_eigenvalue(2);
 
                 //comparison to 0.01 instead of 0 was proposed by O.Pasternak
 
                 if( eigen_vals[0]>0.01 && eigen_vals[1]>0.01 && eigen_vals[2]>0.01)
                 {
                   mask->SetPixel(ix,1);
 
                 }
                 else
                 {
                   badvoxels++;
                 }
 
               }
 
           }
          }
        }
 
       double ret = badvoxels;
 
       return ret;
 
   }
 
 
   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,itk::Image<short, 3>::Pointer mask,vnl_vector< double> pixel_max,vnl_vector< double> pixel_min)
   {
     // in this method the voxels that has tensor negative eigenvalues are smoothed. Smoothing is done on DWI image.For the voxel
     //detected as bad one, B0 image is smoothed obligatory. All other gradient images are smoothed only when value of attenuation
     //is out of declared bounds for too high or too low attenuation.
 
     // declaration of important variables
 
 
     vnl_vector<double> org_data(nof-numberb0);
     vnl_vector<double> atten(nof-numberb0);
     double cnt_atten=0;
 
     for (int z=0;z<size[2];z++)
     {
 
       for (int x=0;x<size[0];x++)
       {
 
         for (int y=0;y<size[1];y++)
         {
           itk::Index<3> ix = {x, y, 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];
             }
 
 
             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)
               {
 
                 atten[cnt]=org_data[i]/mean_b;
                 cnt++;
               }
             }
 
             cnt_atten=0;
 
             //smoothing certain gradient images taht are out of declared constraints
 
             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);
 
               }
 
 
               cnt_atten++;
 
              }
               //smoothing B0
               if(m_B0Mask[f]==1)
               {
 
                   org_data[f] = CheckNeighbours(x,y,z,f,size,mask,corrected_diffusion);
 
               }
 
 
             }
 
             for (int i=0;i<nof;i++)
             {
               pt[i]=org_data[i];
             }
 
             corrected_diffusion->SetPixel(ix, pt);
 
           }
           else
           {
               GradientVectorType  pt = corrected_diffusion->GetPixel(ix);
               corrected_diffusion->SetPixel(ix, pt);
 
           }
         }
       }
     }
 
 
 
   }
 
   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++)
           {
 
 
               ix[0] = x; ix[1] = y; ix[2] = z;
 
               mask_val= mask->GetPixel(ix);
 
               //Tensors are calculated only for voxels above theshold for B0 image.
 
               if( mask_val > 0.0 )
               {
 
                  // calculation of attenuation with use of gradient image and  and mean B0 image
                  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++)
                   {
 
                     atten[i]=log((double)atten[i]);
                   }
 
                   // Calculation of tensor with use of previously calculated inverse of design matrix and attenuation
                   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);
 
 
 
               }
               // for voxels with mask value 0 - tensor is simply 0 ( outside brain value)
               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)
   {
     // The method changes voxels in the mask that poses a certain value with other value.
 
     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);
           }
         }
       }
     }
   }
 
 
 
   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