diff --git a/Modules/DiffusionImaging/DiffusionCore/Algorithms/Reconstruction/MultishellProcessing/itkADCAverageFunctor.cpp b/Modules/DiffusionImaging/DiffusionCore/Algorithms/Reconstruction/MultishellProcessing/itkADCAverageFunctor.cpp
index 1605ef0d52..ac8d3f9a53 100644
--- a/Modules/DiffusionImaging/DiffusionCore/Algorithms/Reconstruction/MultishellProcessing/itkADCAverageFunctor.cpp
+++ b/Modules/DiffusionImaging/DiffusionCore/Algorithms/Reconstruction/MultishellProcessing/itkADCAverageFunctor.cpp
@@ -1,56 +1,56 @@
 /*===================================================================
 
 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.
 
 ===================================================================*/
 
 #include "itkADCAverageFunctor.h"
 #include <math.h>
 #include <iostream>
 #include <iomanip>
 
 void itk::ADCAverageFunctor::operator()(vnl_matrix<double> & newSignal, const vnl_matrix<double> & SignalMatrix, const double & S0)
 {
 
   vnl_matrix<double> ADCMatrix(SignalMatrix.rows(),SignalMatrix.cols());
 
   // Calculate ADC for each measurement
   for(unsigned int i = 0 ; i < SignalMatrix.rows(); ++i)
     for(unsigned int j = 0; j < SignalMatrix.cols(); ++j)
       ADCMatrix(i,j) = std::log(SignalMatrix(i,j) / S0) / (-m_BValueList[j]);// D = ln(S/S0)/-b
 
 
   // Calculate new Signal using Average ADC
   for(unsigned int i = 0 ; i < SignalMatrix.rows(); ++i){
     double averageADC = ADCMatrix.get_row(i).mean();
     newSignal.put(i,0, S0 * std::exp(-m_TargetBvalue * averageADC) ); // S = S0*exp(-b*D)
 
     //OUTPUT FOR EVALUATION
     // Root Mean Squares Error
     double error = 0;
     for(unsigned int j = 0 ; j < SignalMatrix.cols(); ++j)
-      error += std::pow( S0 * std::exp(-m_BValueList[j] * averageADC),2); // sum of squres
+      error += std::pow(SignalMatrix(i,j) - S0 * std::exp(-m_BValueList[j] * averageADC),2); // sum of squres
     error /= (double)SignalMatrix.cols(); // mean
     error = std::sqrt(error);
 
     newSignal.put(i, 1, error ); // RMS Error
 
     /*std::cout << std::scientific << std::setprecision(5)
               << averageADC   << ","                    // AverageADC
               << S0           << ","                    // S0 value
               << error        << ",";                   // End error
     for(unsigned int j = 0; j < SignalMatrix.get_row(i).size(); j++ )
       std::cout << std::scientific << std::setprecision(5) << SignalMatrix.get_row(i)[j] << ",";    // S_n Values corresponding to shell 1 to shell n
     std::cout << std::endl;*/
   }
 }