void itk::BiExpFitFunctor::operator()(vnl_matrix<double> & newSignal,const vnl_matrix<double> & SignalMatrix, const double & S0)
{
vnl_vector<double> initalGuess(3);
// initialize Least Squres Function
// SignalMatrix.cols() defines the number of shells points
lestSquaresFunction model(SignalMatrix.cols());
model.set_bvalues(m_BValueList);// set BValue Vector e.g.: [1000, 2000, 3000] <- shell b Values
// initialize Levenberg Marquardt
vnl_levenberg_marquardt minimizer(model);
minimizer.set_max_function_evals(1000); // Iterations
minimizer.set_f_tolerance(1e-10); // Function tolerance
// for each Direction calculate LSF Coeffs ADC & AKC
for(unsigned int i = 0 ; i < SignalMatrix.rows(); i++)
{
model.set_measurements(SignalMatrix.get_row(i));
model.set_reference_measurement(S0);
initalGuess.put(0, 0.f); // ADC_slow
initalGuess.put(1, 0.009f); // ADC_fast
initalGuess.put(2, 0.7f); // lambda
// start Levenberg-Marquardt
minimizer.minimize_without_gradient(initalGuess);
const double & ADC_slow = initalGuess.get(0);
const double & ADC_fast = initalGuess.get(1);
const double & lambda = initalGuess(2);
newSignal.put(i, 0, S0 * (lambda * std::exp(-m_TargetBvalue * ADC_slow) + (1-lambda)* std::exp(-m_TargetBvalue * ADC_fast)));
newSignal.put(i, 1, minimizer.get_end_error()); // RMS Error
//OUTPUT FOR EVALUATION
/*std::cout << std::scientific << std::setprecision(5)
<< ADC_slow << "," // lambda
<< ADC_fast << "," // alpha
<< lambda << "," // lambda
<< S0 << "," // S0 value
<< minimizer.get_end_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
if(j != SignalMatrix.get_row(i).size()-1) std::cout << ",";
}
std::cout << std::endl;*/
}
}
