diff --git a/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/FitFibersToImage.cpp b/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/FitFibersToImage.cpp
index c330b1ddaf..eedda228b1 100755
--- a/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/FitFibersToImage.cpp
+++ b/Modules/DiffusionImaging/FiberTracking/cmdapps/TractographyEvaluation/FitFibersToImage.cpp
@@ -1,742 +1,745 @@
/*===================================================================
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 <mitkBaseData.h>
#include <mitkImageCast.h>
#include <mitkImageToItk.h>
#include <metaCommand.h>
#include <mitkCommandLineParser.h>
#include <usAny.h>
#include <mitkIOUtil.h>
#include <boost/lexical_cast.hpp>
#include <itksys/SystemTools.hxx>
#include <itkDirectory.h>
#include <mitkFiberBundle.h>
#include <mitkPreferenceListReaderOptionsFunctor.h>
#include <mitkDiffusionPropertyHelper.h>
#include <vnl/vnl_linear_system.h>
#include <Eigen/Dense>
#include <mitkStickModel.h>
#include <mitkBallModel.h>
#include <vigra/regression.hxx>
#include <itkImageFileWriter.h>
#include <itkImageDuplicator.h>
#include <itkMersenneTwisterRandomVariateGenerator.h>
#include <mitkPeakImage.h>
#include <vnl/algo/vnl_lbfgsb.h>
#include <vnl/vnl_sparse_matrix.h>
#include <vnl/vnl_sparse_matrix_linear_system.h>
#include <vnl/algo/vnl_lsqr.h>
#include <itkImageDuplicator.h>
#include <itkTimeProbe.h>
#include <random>
#include <itkParticleSwarmOptimizer.h>
#include <itkOnePlusOneEvolutionaryOptimizer.h>
#include <itkGradientDescentOptimizer.h>
#include <itkSPSAOptimizer.h>
using namespace std;
typedef itksys::SystemTools ist;
typedef itk::Point<float, 4> PointType4;
typedef itk::Image< float, 4 > PeakImgType;
vnl_vector_fixed<float,3> GetClosestPeak(itk::Index<4> idx, PeakImgType::Pointer peak_image , vnl_vector_fixed<float,3> fiber_dir, int& id, double& w )
{
int m_NumDirs = peak_image->GetLargestPossibleRegion().GetSize()[3]/3;
vnl_vector_fixed<float,3> out_dir; out_dir.fill(0);
float angle = 0.8;
for (int i=0; i<m_NumDirs; i++)
{
vnl_vector_fixed<float,3> dir;
idx[3] = i*3;
dir[0] = peak_image->GetPixel(idx);
idx[3] += 1;
dir[1] = peak_image->GetPixel(idx);
idx[3] += 1;
dir[2] = peak_image->GetPixel(idx);
float mag = dir.magnitude();
if (mag<mitk::eps)
continue;
dir.normalize();
float a = dot_product(dir, fiber_dir);
if (fabs(a)>angle)
{
angle = fabs(a);
w = angle;
if (a<0)
out_dir = -dir;
else
out_dir = dir;
out_dir *= mag;
id = i;
}
}
return out_dir;
}
class VnlCostFunction : public vnl_cost_function
{
public:
vnl_sparse_matrix_linear_system< double >* S;
vnl_sparse_matrix< double > m_A;
vnl_sparse_matrix< double > m_A_Ones; // matrix indicating active weights with 1
vnl_vector< double > m_b;
double m_Lambda; // regularization factor
vnl_vector<double> row_sums; // number of active weights per row
vnl_vector<double> local_weight_means; // mean weight of each row
void SetProblem(vnl_sparse_matrix< double >& A, vnl_vector<double>& b, double lambda)
{
S = new vnl_sparse_matrix_linear_system<double>(A, b);
m_A = A;
m_b = b;
m_Lambda = lambda;
m_A_Ones.set_size(m_A.rows(), m_A.cols());
m_A.reset();
while (m_A.next())
m_A_Ones.put(m_A.getrow(), m_A.getcolumn(), 1);
unsigned int N = m_b.size();
vnl_vector<double> ones; ones.set_size(dim); ones.fill(1.0);
row_sums.set_size(N);
m_A_Ones.mult(ones, row_sums);
local_weight_means.set_size(N);
}
VnlCostFunction(const int NumVars) : vnl_cost_function(NumVars)
{
}
void regu_MSE(vnl_vector<double> const &x, double& cost)
{
double mean = x.mean();
vnl_vector<double> tx = x-mean;
cost += m_Lambda*1e8*tx.squared_magnitude()/x.size();
}
void regu_MSM(vnl_vector<double> const &x, double& cost)
{
cost += m_Lambda*1e8*x.squared_magnitude()/x.size();
}
void regu_localMSE(vnl_vector<double> const &x, double& cost)
{
m_A_Ones.mult(x, local_weight_means);
local_weight_means = element_quotient(local_weight_means, row_sums);
m_A_Ones.reset();
unsigned int num_elements = 0;
double regu = 0;
while (m_A_Ones.next())
{
double d = 0;
if (x[m_A_Ones.getcolumn()]>local_weight_means[m_A_Ones.getrow()])
d = std::exp(x[m_A_Ones.getcolumn()]) - std::exp(local_weight_means[m_A_Ones.getrow()]);
else
d = x[m_A_Ones.getcolumn()] - local_weight_means[m_A_Ones.getrow()];
regu += d*d;
++num_elements;
}
cost += m_Lambda*1e3*regu/num_elements;
}
void grad_regu_MSE(vnl_vector<double> const &x, vnl_vector<double> &dx)
{
double mean = x.mean();
vnl_vector<double> tx = x-mean;
vnl_vector<double> tx2(dim, 0.0);
vnl_vector<double> h(dim, 1.0);
for (int c=0; c<dim; c++)
{
h[c] = dim-1;
tx2[c] += dot_product(h,tx);
h[c] = 1;
}
dx += tx2*m_Lambda*1e8*2.0/(dim*dim);
}
void grad_regu_MSM(vnl_vector<double> const &x, vnl_vector<double> &dx)
{
dx += m_Lambda*1e8*2.0*x/dim;
}
void grad_regu_localMSE(vnl_vector<double> const &x, vnl_vector<double> &dx)
{
m_A_Ones.mult(x, local_weight_means);
local_weight_means = element_quotient(local_weight_means, row_sums);
vnl_vector<double> exp_x = x.apply(std::exp);
vnl_vector<double> exp_means = local_weight_means.apply(std::exp);
vnl_vector<double> tdx(dim, 0);
m_A_Ones.reset();
while (m_A_Ones.next())
{
int c = m_A_Ones.getcolumn();
int r = m_A_Ones.getrow();
if (row_sums[r]==0)
continue;
if (x[c]>local_weight_means[r])
tdx[c] += (exp_x[c] * ( exp_x[c] - exp_means[r] ))/row_sums[r];
else
tdx[c] += (x[c] - local_weight_means[r])/row_sums[r];
}
dx += tdx*1e3*2.0*m_Lambda;
// vnl_vector<double> dr; dr.set_size(dim); dr.fill(0);
// for (unsigned int r=0; r<m_A_Ones.rows(); r++)
// {
// int n = row_sums[r];
// vnl_vector<double> weights; weights.set_size(n);
// vnl_matrix<double> temp(n,n,1); temp.fill_diagonal(n-1);
// int i=0;
// for (auto w : m_A_Ones.get_row(r))
// {
// weights[i]=w.second;
// ++i;
// }
// weights -= local_weight_means[r];
// weights = temp*weights;
// i=0;
// for (auto w : m_A_Ones.get_row(r))
// {
// dr[w.second] += weights[i];
// ++i;
// }
// }
// dx += dr*2.0*m_Lambda;
}
double f(vnl_vector<double> const &x)
{
double cost = S->get_rms_error(x);
cost *= cost;
// cost for e^x
// vnl_vector<double> x_exp; x_exp.set_size(x.size());
// for (unsigned int c=0; c<x.size(); c++)
// x_exp[c] = std::exp(x[c]);
// double cost = S->get_rms_error(x_exp);
// cost *= cost;
regu_localMSE(x, cost);
// regu_MSM(x, cost);
return cost;
}
void gradf(vnl_vector<double> const &x, vnl_vector<double> &dx)
{
dx.fill(0.0);
unsigned int N = m_b.size();
// vnl_vector<double> x_exp; x_exp.set_size(x.size());
// for (unsigned int c=0; c<x.size(); c++)
// x_exp[c] = std::exp(x[c]);
vnl_vector<double> d; d.set_size(N);
S->multiply(x,d);
d -= m_b;
S->transpose_multiply(d, dx);
dx *= 2.0/N;
// for (unsigned int c=0; c<x.size(); c++)
// dx[c] *= x_exp[c]; // only for e^x weights
grad_regu_localMSE(x,dx);
// grad_regu_MSM(x,dx);
}
};
vnl_vector<double> FitFibers( std::string , std::vector< mitk::FiberBundle::Pointer > input_tracts, mitk::Image::Pointer inputImage, vnl_sparse_matrix< double >& A, vnl_vector<double>& b, bool single_fiber_fit, int max_iter, float g_tol, float lambda )
{
typedef mitk::ImageToItk< PeakImgType > CasterType;
CasterType::Pointer caster = CasterType::New();
caster->SetInput(inputImage);
caster->Update();
PeakImgType::Pointer itkImage = caster->GetOutput();
unsigned int* image_size = inputImage->GetDimensions();
int sz_x = image_size[0];
int sz_y = image_size[1];
int sz_z = image_size[2];
int sz_peaks = image_size[3]/3 + 1; // +1 for zero - peak
int num_voxels = sz_x*sz_y*sz_z;
unsigned int num_unknowns = input_tracts.size();
if (single_fiber_fit)
{
num_unknowns = 0;
for (unsigned int bundle=0; bundle<input_tracts.size(); bundle++)
num_unknowns += input_tracts.at(bundle)->GetNumFibers();
}
unsigned int number_of_residuals = num_voxels * sz_peaks;
// create linear system
MITK_INFO << "Num. unknowns: " << num_unknowns;
MITK_INFO << "Num. residuals: " << number_of_residuals;
MITK_INFO << "Creating system ...";
A.set_size(number_of_residuals, num_unknowns);
b.set_size(number_of_residuals); b.fill(0.0);
double TD = 0;
double FD = 0;
unsigned int dir_count = 0;
unsigned int fiber_count = 0;
for (unsigned int bundle=0; bundle<input_tracts.size(); bundle++)
{
vtkSmartPointer<vtkPolyData> polydata = input_tracts.at(bundle)->GetFiberPolyData();
for (int i=0; i<input_tracts.at(bundle)->GetNumFibers(); ++i)
{
vtkCell* cell = polydata->GetCell(i);
int numPoints = cell->GetNumberOfPoints();
vtkPoints* points = cell->GetPoints();
if (numPoints<2)
MITK_INFO << "FIBER WITH ONLY ONE POINT ENCOUNTERED!";
for (int j=0; j<numPoints-1; ++j)
{
double* p1 = points->GetPoint(j);
PointType4 p;
p[0]=p1[0];
p[1]=p1[1];
p[2]=p1[2];
p[3]=0;
itk::Index<4> idx4;
itkImage->TransformPhysicalPointToIndex(p, idx4);
if (!itkImage->GetLargestPossibleRegion().IsInside(idx4))
continue;
double* p2 = points->GetPoint(j+1);
vnl_vector_fixed<float,3> fiber_dir;
fiber_dir[0] = p[0]-p2[0];
fiber_dir[1] = p[1]-p2[1];
fiber_dir[2] = p[2]-p2[2];
fiber_dir.normalize();
double w = 1;
int peak_id = sz_peaks-1;
vnl_vector_fixed<float,3> odf_peak = GetClosestPeak(idx4, itkImage, fiber_dir, peak_id, w);
float peak_mag = odf_peak.magnitude();
int x = idx4[0];
int y = idx4[1];
int z = idx4[2];
unsigned int linear_index = x + sz_x*y + sz_x*sz_y*z + sz_x*sz_y*sz_z*peak_id;
if (b[linear_index] == 0 && peak_id<3)
{
dir_count++;
FD += peak_mag;
}
TD += w;
if (single_fiber_fit)
{
b[linear_index] = peak_mag;
A.put(linear_index, fiber_count, A.get(linear_index, fiber_count) + w);
}
else
{
b[linear_index] = peak_mag;
A.put(linear_index, bundle, A.get(linear_index, bundle) + w);
}
}
++fiber_count;
}
}
TD /= (dir_count*fiber_count);
FD /= dir_count;
A /= TD;
b *= 100.0/FD; // times 100 because we want to avoid too small values for computational reasons
// MITK_INFO << "TD: " << TD;
// MITK_INFO << "FD: " << FD;
// MITK_INFO << "Regularization: " << lambda;
itk::TimeProbe clock;
clock.Start();
MITK_INFO << "Fitting fibers";
VnlCostFunction cost(num_unknowns);
cost.SetProblem(A, b, lambda);
vnl_vector<double> x; x.set_size(num_unknowns); x.fill( TD/100.0 * FD/2.0 );
vnl_lbfgsb minimizer(cost);
vnl_vector<double> l; l.set_size(num_unknowns); l.fill(0);
vnl_vector<long> bound_selection; bound_selection.set_size(num_unknowns); bound_selection.fill(1);
minimizer.set_bound_selection(bound_selection);
minimizer.set_lower_bound(l);
minimizer.set_trace(true);
minimizer.set_projected_gradient_tolerance(g_tol);
if (max_iter>0)
minimizer.set_max_function_evals(max_iter);
minimizer.minimize(x);
// SECOND RUN
std::vector< double > weights;
for (auto w : x)
weights.push_back(w);
sort(weights.begin(), weights.end());
MITK_INFO << "Setting upper weight bound to " << weights.at(num_unknowns*0.95);
vnl_vector<double> u; u.set_size(num_unknowns); u.fill(weights.at(num_unknowns*0.95));
minimizer.set_upper_bound(u);
bound_selection.fill(2);
minimizer.set_bound_selection(bound_selection);
minimizer.minimize(x);
weights.clear();
for (auto w : x)
weights.push_back(w);
sort(weights.begin(), weights.end());
MITK_INFO << "*************************";
MITK_INFO << "Weight statistics";
MITK_INFO << "Mean: " << x.mean();
MITK_INFO << "Median: " << weights.at(num_unknowns*0.5);
MITK_INFO << "75% quantile: " << weights.at(num_unknowns*0.75);
MITK_INFO << "95% quantile: " << weights.at(num_unknowns*0.95);
MITK_INFO << "99% quantile: " << weights.at(num_unknowns*0.99);
MITK_INFO << "Min: " << weights.at(0);
MITK_INFO << "Max: " << weights.at(num_unknowns-1);
MITK_INFO << "*************************";
MITK_INFO << "NumEvals: " << minimizer.get_num_evaluations();
MITK_INFO << "NumIterations: " << minimizer.get_num_iterations();
MITK_INFO << "Residual cost: " << minimizer.get_end_error();
MITK_INFO << "Final RMS: " << cost.S->get_rms_error(x);
clock.Stop();
int h = clock.GetTotal()/3600;
int m = ((int)clock.GetTotal()%3600)/60;
int s = (int)clock.GetTotal()%60;
MITK_INFO << "Optimization took " << h << "h, " << m << "m and " << s << "s";
// transform back for peak image creation
A *= FD/100.0;
b *= FD/100.0;
return x;
}
std::vector< string > get_file_list(const std::string& path)
{
std::vector< string > file_list;
itk::Directory::Pointer dir = itk::Directory::New();
if (dir->Load(path.c_str()))
{
int n = dir->GetNumberOfFiles();
for (int r = 0; r < n; r++)
{
const char *filename = dir->GetFile(r);
std::string ext = ist::GetFilenameExtension(filename);
if (ext==".fib" || ext==".trk")
file_list.push_back(path + '/' + filename);
}
}
return file_list;
}
+/*!
+\brief Fits the tractogram to the input peak image by assigning a weight to each fiber (similar to https://doi.org/10.1016/j.neuroimage.2015.06.092).
+*/
int main(int argc, char* argv[])
{
mitkCommandLineParser parser;
parser.setTitle("Fit Fibers To Image");
parser.setCategory("Fiber Tracking Evaluation");
parser.setDescription("");
parser.setContributor("MIC");
parser.setArgumentPrefix("--", "-");
parser.addArgument("", "i1", mitkCommandLineParser::StringList, "Input tractograms:", "input tractograms (.fib, vtk ascii file format)", us::Any(), false);
parser.addArgument("", "i2", mitkCommandLineParser::InputFile, "Input peaks:", "input peak image", us::Any(), false);
parser.addArgument("", "o", mitkCommandLineParser::OutputDirectory, "Output:", "output root", us::Any(), false);
parser.addArgument("max_iter", "", mitkCommandLineParser::Int, "Max. iterations:", "maximum number of optimizer iterations", 20);
parser.addArgument("bundle_based", "", mitkCommandLineParser::Bool, "Bundle based fit:", "fit one weight per input tractogram/bundle, not for each fiber", false);
parser.addArgument("min_g", "", mitkCommandLineParser::Float, "Min. g:", "lower termination threshold for gradient magnitude", 1e-5);
parser.addArgument("lambda", "", mitkCommandLineParser::Float, "Lambda:", "weighting factor for regularization", 1.0);
parser.addArgument("save_res", "", mitkCommandLineParser::Bool, "Residuals:", "save residual images", false);
map<string, us::Any> parsedArgs = parser.parseArguments(argc, argv);
if (parsedArgs.size()==0)
return EXIT_FAILURE;
mitkCommandLineParser::StringContainerType fib_files = us::any_cast<mitkCommandLineParser::StringContainerType>(parsedArgs["i1"]);
- string dwiFile = us::any_cast<string>(parsedArgs["i2"]);
+ string peak_file_name = us::any_cast<string>(parsedArgs["i2"]);
string outRoot = us::any_cast<string>(parsedArgs["o"]);
bool single_fib = true;
if (parsedArgs.count("bundle_based"))
single_fib = !us::any_cast<bool>(parsedArgs["bundle_based"]);
bool save_residuals = false;
if (parsedArgs.count("save_res"))
save_residuals = us::any_cast<bool>(parsedArgs["residuals"]);
int max_iter = 20;
if (parsedArgs.count("it"))
max_iter = us::any_cast<int>(parsedArgs["it"]);
float g_tol = 1e-5;
if (parsedArgs.count("min_g"))
g_tol = us::any_cast<float>(parsedArgs["min_g"]);
float lambda = 1.0;
if (parsedArgs.count("lambda"))
lambda = us::any_cast<float>(parsedArgs["lambda"]);
try
{
std::vector< mitk::FiberBundle::Pointer > input_tracts;
mitk::PreferenceListReaderOptionsFunctor functor = mitk::PreferenceListReaderOptionsFunctor({"Peak Image", "Fiberbundles"}, {});
- mitk::Image::Pointer inputImage = dynamic_cast<mitk::PeakImage*>(mitk::IOUtil::Load(dwiFile, &functor)[0].GetPointer());
+ mitk::Image::Pointer inputImage = dynamic_cast<mitk::PeakImage*>(mitk::IOUtil::Load(peak_file_name, &functor)[0].GetPointer());
float minSpacing = 1;
if(inputImage->GetGeometry()->GetSpacing()[0]<inputImage->GetGeometry()->GetSpacing()[1] && inputImage->GetGeometry()->GetSpacing()[0]<inputImage->GetGeometry()->GetSpacing()[2])
minSpacing = inputImage->GetGeometry()->GetSpacing()[0];
else if (inputImage->GetGeometry()->GetSpacing()[1] < inputImage->GetGeometry()->GetSpacing()[2])
minSpacing = inputImage->GetGeometry()->GetSpacing()[1];
else
minSpacing = inputImage->GetGeometry()->GetSpacing()[2];
std::vector< std::string > fib_names;
for (auto item : fib_files)
{
if ( ist::FileIsDirectory(item) )
{
for ( auto fibFile : get_file_list(item) )
{
mitk::FiberBundle::Pointer inputTractogram = dynamic_cast<mitk::FiberBundle*>(mitk::IOUtil::Load(fibFile)[0].GetPointer());
if (inputTractogram.IsNull())
continue;
inputTractogram->ResampleLinear(minSpacing/10);
input_tracts.push_back(inputTractogram);
fib_names.push_back(fibFile);
}
}
else
{
mitk::FiberBundle::Pointer inputTractogram = dynamic_cast<mitk::FiberBundle*>(mitk::IOUtil::Load(item)[0].GetPointer());
if (inputTractogram.IsNull())
continue;
inputTractogram->ResampleLinear(minSpacing/10);
input_tracts.push_back(inputTractogram);
fib_names.push_back(item);
}
}
vnl_sparse_matrix<double> A;
vnl_vector<double> b;
vnl_vector<double> x = FitFibers(outRoot, input_tracts, inputImage, A, b, single_fib, max_iter, g_tol, lambda);
MITK_INFO << "Weighting fibers";
if (single_fib)
{
unsigned int fiber_count = 0;
for (unsigned int bundle=0; bundle<input_tracts.size(); bundle++)
{
for (int i=0; i<input_tracts.at(bundle)->GetNumFibers(); i++)
{
input_tracts.at(bundle)->SetFiberWeight(i, x[fiber_count]);
++fiber_count;
}
}
}
else
{
for (unsigned int i=0; i<fib_names.size(); ++i)
input_tracts.at(i)->SetFiberWeights(x[i]);
}
if (save_residuals)
{
// OUTPUT IMAGES
MITK_INFO << "Generating output images ...";
typedef mitk::ImageToItk< PeakImgType > CasterType;
CasterType::Pointer caster = CasterType::New();
caster->SetInput(inputImage);
caster->Update();
PeakImgType::Pointer peak_image = caster->GetOutput();
itk::ImageDuplicator< PeakImgType >::Pointer duplicator = itk::ImageDuplicator< PeakImgType >::New();
duplicator->SetInputImage(peak_image);
duplicator->Update();
PeakImgType::Pointer underexplained_image = duplicator->GetOutput();
underexplained_image->FillBuffer(0.0);
duplicator->SetInputImage(underexplained_image);
duplicator->Update();
PeakImgType::Pointer overexplained_image = duplicator->GetOutput();
overexplained_image->FillBuffer(0.0);
duplicator->SetInputImage(overexplained_image);
duplicator->Update();
PeakImgType::Pointer residual_image = duplicator->GetOutput();
residual_image->FillBuffer(0.0);
duplicator->SetInputImage(residual_image);
duplicator->Update();
PeakImgType::Pointer fitted_image = duplicator->GetOutput();
fitted_image->FillBuffer(0.0);
vnl_sparse_matrix_linear_system<double> S(A, b);
vnl_vector<double> fitted_b; fitted_b.set_size(b.size());
S.multiply(x, fitted_b);
unsigned int* image_size = inputImage->GetDimensions();
int sz_x = image_size[0];
int sz_y = image_size[1];
int sz_z = image_size[2];
int sz_peaks = image_size[3]/3 + 1; // +1 for zero - peak
for (unsigned int r=0; r<b.size(); r++)
{
itk::Index<4> idx;
unsigned int linear_index = r;
idx[0] = linear_index % sz_x; linear_index /= sz_x;
idx[1] = linear_index % sz_y; linear_index /= sz_y;
idx[2] = linear_index % sz_z; linear_index /= sz_z;
int peak_id = linear_index % sz_peaks;
if (peak_id<sz_peaks-1)
{
vnl_vector_fixed<float,3> peak_dir;
idx[3] = peak_id*3;
peak_dir[0] = peak_image->GetPixel(idx);
idx[3] += 1;
peak_dir[1] = peak_image->GetPixel(idx);
idx[3] += 1;
peak_dir[2] = peak_image->GetPixel(idx);
peak_dir.normalize();
peak_dir *= fitted_b[r];
idx[3] = peak_id*3;
fitted_image->SetPixel(idx, peak_dir[0]);
idx[3] += 1;
fitted_image->SetPixel(idx, peak_dir[1]);
idx[3] += 1;
fitted_image->SetPixel(idx, peak_dir[2]);
}
}
itk::Index<4> idx;
for (idx[0]=0; idx[0]<sz_x; ++idx[0])
for (idx[1]=0; idx[1]<sz_y; ++idx[1])
for (idx[2]=0; idx[2]<sz_z; ++idx[2])
{
vnl_vector_fixed<float,3> peak_dir;
vnl_vector_fixed<float,3> fitted_dir;
for (idx[3]=0; idx[3]<image_size[3]; ++idx[3])
{
peak_dir[idx[3]%3] = peak_image->GetPixel(idx);
fitted_dir[idx[3]%3] = fitted_image->GetPixel(idx);
residual_image->SetPixel(idx, peak_image->GetPixel(idx) - fitted_image->GetPixel(idx));
if (idx[3]%3==2)
{
itk::Index<4> tidx= idx;
if (peak_dir.magnitude()>fitted_dir.magnitude())
{
underexplained_image->SetPixel(tidx, peak_dir[2]-fitted_dir[2]); tidx[3]--;
underexplained_image->SetPixel(tidx, peak_dir[1]-fitted_dir[1]); tidx[3]--;
underexplained_image->SetPixel(tidx, peak_dir[0]-fitted_dir[0]);
}
else
{
overexplained_image->SetPixel(tidx, fitted_dir[2]-peak_dir[2]); tidx[3]--;
overexplained_image->SetPixel(tidx, fitted_dir[1]-peak_dir[1]); tidx[3]--;
overexplained_image->SetPixel(tidx, fitted_dir[0]-peak_dir[0]);
}
}
}
}
itk::ImageFileWriter< PeakImgType >::Pointer writer = itk::ImageFileWriter< PeakImgType >::New();
writer->SetInput(fitted_image);
writer->SetFileName(outRoot + "fitted_image.nrrd");
writer->Update();
writer->SetInput(residual_image);
writer->SetFileName(outRoot + "residual_image.nrrd");
writer->Update();
writer->SetInput(overexplained_image);
writer->SetFileName(outRoot + "overexplained_image.nrrd");
writer->Update();
writer->SetInput(underexplained_image);
writer->SetFileName(outRoot + "underexplained_image.nrrd");
writer->Update();
}
for (unsigned int bundle=0; bundle<input_tracts.size(); bundle++)
{
input_tracts.at(bundle)->Compress(0.1);
std::string name = fib_names.at(bundle);
name = ist::GetFilenameWithoutExtension(name);
mitk::IOUtil::Save(input_tracts.at(bundle), outRoot + name + "_fitted.fib");
}
}
catch (itk::ExceptionObject e)
{
std::cout << e;
return EXIT_FAILURE;
}
catch (std::exception e)
{
std::cout << e.what();
return EXIT_FAILURE;
}
catch (...)
{
std::cout << "ERROR!?!";
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}