Examples/RegistrationITKv4/DeformableRegistration15.cxx

/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         https://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
 
// Software Guide : BeginLatex
//
// This example illustrates a realistic pipeline for solving a full deformable
// registration problem.
//
// First the two images are roughly aligned by using a transform
// initialization, then they are registered using a rigid transform, that in
// turn, is used to initialize a registration with an affine transform. The
// transform resulting from the affine registration is compounded with
// a BSplineTransform. The deformable registration is computed,
// and finally the resulting transform is used to resample the moving image.
//
// Software Guide : EndLatex
 
#include "itkImageRegistrationMethod.h"
#include "itkMattesMutualInformationImageToImageMetric.h"
 
#include "itkTimeProbesCollectorBase.h"
#include "itkMemoryProbesCollectorBase.h"
 
//  Software Guide : BeginLatex
//
//  The following are the most relevant headers to this example.
//
//  \index{itk::VersorRigid3DTransform!header}
//  \index{itk::AffineTransform!header}
//  \index{itk::BSplineTransform!header}
//  \index{itk::RegularStepGradientDescentOptimizer!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkCenteredTransformInitializer.h"
#include "itkVersorRigid3DTransform.h"
#include "itkAffineTransform.h"
#include "itkBSplineTransform.h"
#include "itkCompositeTransform.h"
#include "itkRegularStepGradientDescentOptimizer.h"
// Software Guide : EndCodeSnippet
 
#include "itkBSplineResampleImageFunction.h"
#include "itkBSplineDecompositionImageFilter.h"
 
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
 
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSquaredDifferenceImageFilter.h"
#include "itkSqrtImageFilter.h"
 
#include "itkTransformFileWriter.h"
 
//  The following section of code implements a Command observer
//  used to monitor the evolution of the registration process.
#include "itkCommand.h"
class CommandIterationUpdate : public itk::Command
{
public:
  using Self = CommandIterationUpdate;
  using Superclass = itk::Command;
  using Pointer = itk::SmartPointer<Self>;
  itkNewMacro(Self);
 
protected:
  CommandIterationUpdate() = default;
 
public:
  using OptimizerType = itk::RegularStepGradientDescentOptimizer;
  using OptimizerPointer = const OptimizerType *;
 
  void
  Execute(itk::Object * caller, const itk::EventObject & event) override
  {
    Execute((const itk::Object *)caller, event);
  }
 
  void
  Execute(const itk::Object * object, const itk::EventObject & event) override
  {
    auto optimizer = static_cast<OptimizerPointer>(object);
    if (!(itk::IterationEvent().CheckEvent(&event)))
    {
      return;
    }
    std::cout << optimizer->GetCurrentIteration() << "   ";
    std::cout << optimizer->GetValue() << "   ";
    std::cout << std::endl;
  }
};
 
int
main(int argc, char * argv[])
{
  if (argc < 4)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile outputImagefile  ";
    std::cerr << " [differenceOutputfile] [differenceBeforeRegistration] ";
    std::cerr << " [filenameForFinalTransformParameters] ";
    std::cerr << " [useExplicitPDFderivatives ] [useCachingBSplineWeights ] ";
    std::cerr << " [deformationField] ";
    std::cerr << " [numberOfGridNodesInsideImageInOneDimensionCoarse] ";
    std::cerr << " [numberOfGridNodesInsideImageInOneDimensionFine] ";
    std::cerr << " [maximumStepLength] [maximumNumberOfIterations]";
    std::cerr << std::endl;
    return EXIT_FAILURE;
  }
 
  constexpr unsigned int ImageDimension = 3;
  using PixelType = short;
 
  using FixedImageType = itk::Image<PixelType, ImageDimension>;
  using MovingImageType = itk::Image<PixelType, ImageDimension>;
 
 
  constexpr unsigned int SpaceDimension = ImageDimension;
 
  constexpr unsigned int SplineOrder = 3;
  using CoordinateRepType = double;
 
  using RigidTransformType = itk::VersorRigid3DTransform<double>;
 
  using AffineTransformType = itk::AffineTransform<double, SpaceDimension>;
 
  using DeformableTransformType =
    itk::BSplineTransform<CoordinateRepType, SpaceDimension, SplineOrder>;
 
  using TransformInitializerType =
    itk::CenteredTransformInitializer<RigidTransformType,
                                      FixedImageType,
                                      MovingImageType>;
 
 
  using OptimizerType = itk::RegularStepGradientDescentOptimizer;
 
 
  using MetricType =
    itk::MattesMutualInformationImageToImageMetric<FixedImageType,
                                                   MovingImageType>;
 
  using InterpolatorType =
    itk::LinearInterpolateImageFunction<MovingImageType, double>;
 
  using RegistrationType =
    itk::ImageRegistrationMethod<FixedImageType, MovingImageType>;
 
  auto metric = MetricType::New();
  auto optimizer = OptimizerType::New();
  auto interpolator = InterpolatorType::New();
  auto registration = RegistrationType::New();
 
 
  registration->SetMetric(metric);
  registration->SetOptimizer(optimizer);
  registration->SetInterpolator(interpolator);
 
  // Auxiliary identity transform.
  using IdentityTransformType =
    itk::IdentityTransform<double, SpaceDimension>;
  auto identityTransform = IdentityTransformType::New();
 
 
  //   Read the Fixed and Moving images.
  using FixedImageReaderType = itk::ImageFileReader<FixedImageType>;
  using MovingImageReaderType = itk::ImageFileReader<MovingImageType>;
 
  auto fixedImageReader = FixedImageReaderType::New();
  auto movingImageReader = MovingImageReaderType::New();
 
  fixedImageReader->SetFileName(argv[1]);
  movingImageReader->SetFileName(argv[2]);
 
  try
  {
    fixedImageReader->Update();
    movingImageReader->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
 
  const FixedImageType::ConstPointer fixedImage =
    fixedImageReader->GetOutput();
 
  registration->SetFixedImage(fixedImage);
  registration->SetMovingImage(movingImageReader->GetOutput());
 
  // Add a time and memory probes collector for profiling the computation time
  // of every stage.
  itk::TimeProbesCollectorBase   chronometer;
  itk::MemoryProbesCollectorBase memorymeter;
 
 
  // Setup the metric parameters
  metric->SetNumberOfHistogramBins(50);
 
  const FixedImageType::RegionType fixedRegion =
    fixedImage->GetBufferedRegion();
 
  const unsigned int numberOfPixels = fixedRegion.GetNumberOfPixels();
 
  metric->ReinitializeSeed(76926294);
 
 
  if (argc > 7)
  {
    // Define whether to calculate the metric derivative by explicitly
    // computing the derivatives of the joint PDF with respect to the
    // Transform parameters, or doing it by progressively accumulating
    // contributions from each bin in the joint PDF.
    metric->SetUseExplicitPDFDerivatives(std::stoi(argv[7]));
  }
 
  if (argc > 8)
  {
    // Define whether to cache the BSpline weights and indexes corresponding
    // to each one of the samples used to compute the metric. Enabling caching
    // will make the algorithm run faster but it will have a cost on the
    // amount of memory that needs to be allocated. This option is only
    // relevant when using the BSplineTransform.
    metric->SetUseCachingOfBSplineWeights(std::stoi(argv[8]));
  }
 
 
  //  Initialize a rigid transform by using Image Intensity Moments
  auto initializer = TransformInitializerType::New();
 
  auto rigidTransform = RigidTransformType::New();
 
  initializer->SetTransform(rigidTransform);
  initializer->SetFixedImage(fixedImageReader->GetOutput());
  initializer->SetMovingImage(movingImageReader->GetOutput());
  initializer->MomentsOn();
 
 
  std::cout << "Starting Rigid Transform Initialization " << std::endl;
 
  memorymeter.Start("Rigid Initialization");
  chronometer.Start("Rigid Initialization");
 
  initializer->InitializeTransform();
 
  chronometer.Stop("Rigid Initialization");
  memorymeter.Stop("Rigid Initialization");
 
  std::cout << "Rigid Transform Initialization completed" << std::endl;
  std::cout << std::endl;
 
  registration->SetFixedImageRegion(fixedRegion);
  registration->SetInitialTransformParameters(
    rigidTransform->GetParameters());
 
  registration->SetTransform(rigidTransform);
 
  //  Define optimizer normalization to compensate for different dynamic range
  //  of rotations and translations.
  using OptimizerScalesType = OptimizerType::ScalesType;
  OptimizerScalesType optimizerScales(
    rigidTransform->GetNumberOfParameters());
  constexpr double translationScale = 1.0 / 1000.0;
 
  optimizerScales[0] = 1.0;
  optimizerScales[1] = 1.0;
  optimizerScales[2] = 1.0;
  optimizerScales[3] = translationScale;
  optimizerScales[4] = translationScale;
  optimizerScales[5] = translationScale;
 
  optimizer->SetScales(optimizerScales);
 
  optimizer->SetMaximumStepLength(0.2000);
  optimizer->SetMinimumStepLength(0.0001);
 
  optimizer->SetNumberOfIterations(200);
 
  //
  // The rigid transform has 6 parameters we use therefore a few samples to
  // run this stage.
  //
  // Regulating the number of samples in the Metric is equivalent to
  // performing multi-resolution registration because it is indeed a
  // sub-sampling of the image.
  metric->SetNumberOfSpatialSamples(10000L);
 
  // Create the Command observer and register it with the optimizer.
  auto observer = CommandIterationUpdate::New();
  optimizer->AddObserver(itk::IterationEvent(), observer);
 
 
  std::cout << "Starting Rigid Registration " << std::endl;
 
  try
  {
    memorymeter.Start("Rigid Registration");
    chronometer.Start("Rigid Registration");
 
    registration->Update();
 
    chronometer.Stop("Rigid Registration");
    memorymeter.Stop("Rigid Registration");
 
    std::cout << "Optimizer stop condition = "
              << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
 
  std::cout << "Rigid Registration completed" << std::endl;
  std::cout << std::endl;
 
  rigidTransform->SetParameters(registration->GetLastTransformParameters());
 
 
  //  Perform Affine Registration
  auto affineTransform = AffineTransformType::New();
 
  affineTransform->SetCenter(rigidTransform->GetCenter());
  affineTransform->SetTranslation(rigidTransform->GetTranslation());
  affineTransform->SetMatrix(rigidTransform->GetMatrix());
 
  registration->SetTransform(affineTransform);
  registration->SetInitialTransformParameters(
    affineTransform->GetParameters());
 
  optimizerScales =
    OptimizerScalesType(affineTransform->GetNumberOfParameters());
 
  optimizerScales[0] = 1.0;
  optimizerScales[1] = 1.0;
  optimizerScales[2] = 1.0;
  optimizerScales[3] = 1.0;
  optimizerScales[4] = 1.0;
  optimizerScales[5] = 1.0;
  optimizerScales[6] = 1.0;
  optimizerScales[7] = 1.0;
  optimizerScales[8] = 1.0;
 
  optimizerScales[9] = translationScale;
  optimizerScales[10] = translationScale;
  optimizerScales[11] = translationScale;
 
  optimizer->SetScales(optimizerScales);
 
  optimizer->SetMaximumStepLength(0.2000);
  optimizer->SetMinimumStepLength(0.0001);
 
  optimizer->SetNumberOfIterations(200);
 
  // The Affine transform has 12 parameters we use therefore a more samples to
  // run this stage.
  //
  // Regulating the number of samples in the Metric is equivalent to
  // performing multi-resolution registration because it is indeed a
  // sub-sampling of the image.
  metric->SetNumberOfSpatialSamples(50000L);
 
 
  std::cout << "Starting Affine Registration " << std::endl;
 
  try
  {
    memorymeter.Start("Affine Registration");
    chronometer.Start("Affine Registration");
 
    registration->Update();
 
    chronometer.Stop("Affine Registration");
    memorymeter.Stop("Affine Registration");
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
 
  std::cout << "Affine Registration completed" << std::endl;
  std::cout << std::endl;
 
  affineTransform->SetParameters(registration->GetLastTransformParameters());
 
 
  //  Perform Deformable Registration
  auto bsplineTransformCoarse = DeformableTransformType::New();
 
  constexpr unsigned int numberOfGridNodesInOneDimensionCoarse = 5;
 
  DeformableTransformType::PhysicalDimensionsType fixedPhysicalDimensions;
  DeformableTransformType::MeshSizeType           meshSize;
  DeformableTransformType::OriginType             fixedOrigin;
 
  for (unsigned int i = 0; i < SpaceDimension; ++i)
  {
    fixedOrigin[i] = fixedImage->GetOrigin()[i];
    fixedPhysicalDimensions[i] =
      fixedImage->GetSpacing()[i] *
      static_cast<double>(
        fixedImage->GetLargestPossibleRegion().GetSize()[i] - 1);
  }
  meshSize.Fill(numberOfGridNodesInOneDimensionCoarse - SplineOrder);
 
  bsplineTransformCoarse->SetTransformDomainOrigin(fixedOrigin);
  bsplineTransformCoarse->SetTransformDomainPhysicalDimensions(
    fixedPhysicalDimensions);
  bsplineTransformCoarse->SetTransformDomainMeshSize(meshSize);
  bsplineTransformCoarse->SetTransformDomainDirection(
    fixedImage->GetDirection());
 
  using ParametersType = DeformableTransformType::ParametersType;
 
  unsigned int numberOfBSplineParameters =
    bsplineTransformCoarse->GetNumberOfParameters();
 
 
  optimizerScales = OptimizerScalesType(numberOfBSplineParameters);
  optimizerScales.Fill(1.0);
 
  optimizer->SetScales(optimizerScales);
 
 
  ParametersType initialDeformableTransformParameters(
    numberOfBSplineParameters);
  initialDeformableTransformParameters.Fill(0.0);
 
  using CompositeTransformType =
    itk::CompositeTransform<double, SpaceDimension>;
  auto compositeTransform = CompositeTransformType::New();
  compositeTransform->AddTransform(affineTransform);
  compositeTransform->AddTransform(bsplineTransformCoarse);
  compositeTransform->SetOnlyMostRecentTransformToOptimizeOn();
 
  bsplineTransformCoarse->SetParameters(initialDeformableTransformParameters);
 
  registration->SetInitialTransformParameters(
    bsplineTransformCoarse->GetParameters());
  registration->SetTransform(compositeTransform);
 
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Next we set the parameters of the RegularStepGradientDescentOptimizer
  //  object.
  //
  //  Software Guide : EndLatex
 
 
  // Software Guide : BeginCodeSnippet
  optimizer->SetMaximumStepLength(10.0);
  optimizer->SetMinimumStepLength(0.01);
 
  optimizer->SetRelaxationFactor(0.7);
  optimizer->SetNumberOfIterations(50);
  // Software Guide : EndCodeSnippet
 
 
  // Optionally, get the step length from the command line arguments
  if (argc > 11)
  {
    optimizer->SetMaximumStepLength(std::stod(argv[12]));
  }
 
  // Optionally, get the number of iterations from the command line arguments
  if (argc > 12)
  {
    optimizer->SetNumberOfIterations(std::stoi(argv[13]));
  }
 
 
  // The BSpline transform has a large number of parameters, we use therefore
  // a much larger number of samples to run this stage.
  //
  // Regulating the number of samples in the Metric is equivalent to
  // performing multi-resolution registration because it is indeed a
  // sub-sampling of the image.
  metric->SetNumberOfSpatialSamples(numberOfBSplineParameters * 100);
 
  std::cout << std::endl
            << "Starting Deformable Registration Coarse Grid" << std::endl;
 
  try
  {
    memorymeter.Start("Deformable Registration Coarse");
    chronometer.Start("Deformable Registration Coarse");
 
    registration->Update();
 
    chronometer.Stop("Deformable Registration Coarse");
    memorymeter.Stop("Deformable Registration Coarse");
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
 
  std::cout << "Deformable Registration Coarse Grid completed" << std::endl;
  std::cout << std::endl;
 
  OptimizerType::ParametersType finalParameters =
    registration->GetLastTransformParameters();
 
  bsplineTransformCoarse->SetParameters(finalParameters);
 
  //  Software Guide : BeginLatex
  //
  //  Once the registration has finished with the low resolution grid, we
  //  proceed to instantiate a higher resolution
  //  \code{BSplineTransform}.
  //
  //  Software Guide : EndLatex
 
  auto bsplineTransformFine = DeformableTransformType::New();
 
  constexpr unsigned int numberOfGridNodesInOneDimensionFine = 20;
 
  meshSize.Fill(numberOfGridNodesInOneDimensionFine - SplineOrder);
 
  bsplineTransformFine->SetTransformDomainOrigin(fixedOrigin);
  bsplineTransformFine->SetTransformDomainPhysicalDimensions(
    fixedPhysicalDimensions);
  bsplineTransformFine->SetTransformDomainMeshSize(meshSize);
  bsplineTransformFine->SetTransformDomainDirection(
    fixedImage->GetDirection());
 
  numberOfBSplineParameters = bsplineTransformFine->GetNumberOfParameters();
 
  ParametersType parametersHigh(numberOfBSplineParameters);
  parametersHigh.Fill(0.0);
 
  //  Software Guide : BeginLatex
  //
  //  Now we need to initialize the BSpline coefficients of the higher
  //  resolution transform. This is done by first computing the actual
  //  deformation field at the higher resolution from the lower resolution
  //  BSpline coefficients. Then a BSpline decomposition is done to obtain the
  //  BSpline coefficient of the higher resolution transform.
  //
  //  Software Guide : EndLatex
 
  unsigned int counter = 0;
 
  for (unsigned int k = 0; k < SpaceDimension; ++k)
  {
    using ParametersImageType = DeformableTransformType::ImageType;
    using ResamplerType =
      itk::ResampleImageFilter<ParametersImageType, ParametersImageType>;
    auto upsampler = ResamplerType::New();
 
    using FunctionType =
      itk::BSplineResampleImageFunction<ParametersImageType, double>;
    auto function = FunctionType::New();
 
    upsampler->SetInput(bsplineTransformCoarse->GetCoefficientImages()[k]);
    upsampler->SetInterpolator(function);
    upsampler->SetTransform(identityTransform);
    upsampler->SetSize(bsplineTransformFine->GetCoefficientImages()[k]
                         ->GetLargestPossibleRegion()
                         .GetSize());
    upsampler->SetOutputSpacing(
      bsplineTransformFine->GetCoefficientImages()[k]->GetSpacing());
    upsampler->SetOutputOrigin(
      bsplineTransformFine->GetCoefficientImages()[k]->GetOrigin());
 
    using DecompositionType =
      itk::BSplineDecompositionImageFilter<ParametersImageType,
                                           ParametersImageType>;
    auto decomposition = DecompositionType::New();
 
    decomposition->SetSplineOrder(SplineOrder);
    decomposition->SetInput(upsampler->GetOutput());
    decomposition->Update();
 
    const ParametersImageType::Pointer newCoefficients =
      decomposition->GetOutput();
 
    // copy the coefficients into the parameter array
    using Iterator = itk::ImageRegionIterator<ParametersImageType>;
    Iterator it(newCoefficients,
                bsplineTransformFine->GetCoefficientImages()[k]
                  ->GetLargestPossibleRegion());
    while (!it.IsAtEnd())
    {
      parametersHigh[counter++] = it.Get();
      ++it;
    }
  }
 
 
  optimizerScales = OptimizerScalesType(numberOfBSplineParameters);
  optimizerScales.Fill(1.0);
 
  optimizer->SetScales(optimizerScales);
 
  bsplineTransformFine->SetParameters(parametersHigh);
 
  //  Software Guide : BeginLatex
  //
  //  We now pass the parameters of the high resolution transform as the
  //  initial parameters to be used in a second stage of the registration
  //  process.
  //
  //  Software Guide : EndLatex
 
  std::cout << "Starting Registration with high resolution transform"
            << std::endl;
 
  // Software Guide : BeginCodeSnippet
  compositeTransform->RemoveTransform(); // remove bsplineTransformCoarse
  compositeTransform->AddTransform(bsplineTransformFine);
  compositeTransform->SetOnlyMostRecentTransformToOptimizeOn();
  registration->SetInitialTransformParameters(
    bsplineTransformFine->GetParameters());
  //
  // The BSpline transform at fine scale has a very large number of
  // parameters, we use therefore a much larger number of samples to run this
  // stage. In this case, however, the number of transform parameters is
  // closer to the number of pixels in the image. Therefore we use the
  // geometric mean of the two numbers to ensure that the number of samples is
  // larger than the number of transform parameters and smaller than the
  // number of samples.
  //
  // Regulating the number of samples in the Metric is equivalent to
  // performing multi-resolution registration because it is indeed a
  // sub-sampling of the image.
  const auto numberOfSamples = static_cast<unsigned long>(
    std::sqrt(static_cast<double>(numberOfBSplineParameters) *
              static_cast<double>(numberOfPixels)));
  metric->SetNumberOfSpatialSamples(numberOfSamples);
 
  try
  {
    memorymeter.Start("Deformable Registration Fine");
    chronometer.Start("Deformable Registration Fine");
    registration->Update();
    chronometer.Stop("Deformable Registration Fine");
    memorymeter.Stop("Deformable Registration Fine");
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
  // Software Guide : EndCodeSnippet
 
  std::cout << "Deformable Registration Fine Grid completed" << std::endl;
  std::cout << std::endl;
 
 
  // Report the time and memory taken by the registration
  chronometer.Report(std::cout);
  memorymeter.Report(std::cout);
 
  finalParameters = registration->GetLastTransformParameters();
 
  bsplineTransformFine->SetParameters(finalParameters);
 
 
  using ResampleFilterType =
    itk::ResampleImageFilter<MovingImageType, FixedImageType>;
 
  auto resample = ResampleFilterType::New();
 
  resample->SetTransform(bsplineTransformFine);
  resample->SetInput(movingImageReader->GetOutput());
 
  resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
  resample->SetOutputOrigin(fixedImage->GetOrigin());
  resample->SetOutputSpacing(fixedImage->GetSpacing());
  resample->SetOutputDirection(fixedImage->GetDirection());
 
  // This value is set to zero in order to make easier to perform
  // regression testing in this example. However, for didactic
  // exercise it will be better to set it to a medium gray value
  // such as 100 or 128.
  resample->SetDefaultPixelValue(0);
 
  using OutputPixelType = short;
 
  using OutputImageType = itk::Image<OutputPixelType, ImageDimension>;
 
  using CastFilterType =
    itk::CastImageFilter<FixedImageType, OutputImageType>;
 
  using WriterType = itk::ImageFileWriter<OutputImageType>;
 
 
  auto writer = WriterType::New();
  auto caster = CastFilterType::New();
 
 
  writer->SetFileName(argv[3]);
 
 
  caster->SetInput(resample->GetOutput());
  writer->SetInput(caster->GetOutput());
 
  std::cout << "Writing resampled moving image...";
 
  try
  {
    writer->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
 
  std::cout << " Done!" << std::endl;
 
 
  using DifferenceFilterType =
    itk::SquaredDifferenceImageFilter<FixedImageType,
                                      FixedImageType,
                                      OutputImageType>;
 
  auto difference = DifferenceFilterType::New();
  using SqrtFilterType =
    itk::SqrtImageFilter<OutputImageType, OutputImageType>;
  auto sqrtFilter = SqrtFilterType::New();
  sqrtFilter->SetInput(difference->GetOutput());
 
  using DifferenceImageWriterType = itk::ImageFileWriter<OutputImageType>;
 
  auto writer2 = DifferenceImageWriterType::New();
  writer2->SetInput(sqrtFilter->GetOutput());
 
 
  // Compute the difference image between the
  // fixed and resampled moving image.
  if (argc > 4)
  {
    difference->SetInput1(fixedImageReader->GetOutput());
    difference->SetInput2(resample->GetOutput());
    writer2->SetFileName(argv[4]);
 
    std::cout << "Writing difference image after registration...";
 
    try
    {
      writer2->Update();
    }
    catch (const itk::ExceptionObject & err)
    {
      std::cerr << "ExceptionObject caught !" << std::endl;
      std::cerr << err << std::endl;
      return EXIT_FAILURE;
    }
 
    std::cout << " Done!" << std::endl;
  }
 
 
  // Compute the difference image between the
  // fixed and moving image before registration.
  if (argc > 5)
  {
    writer2->SetFileName(argv[5]);
    difference->SetInput1(fixedImageReader->GetOutput());
    resample->SetTransform(identityTransform);
 
    std::cout << "Writing difference image before registration...";
 
    try
    {
      writer2->Update();
    }
    catch (const itk::ExceptionObject & err)
    {
      std::cerr << "ExceptionObject caught !" << std::endl;
      std::cerr << err << std::endl;
      return EXIT_FAILURE;
    }
 
    std::cout << " Done!" << std::endl;
  }
 
  // Generate the explicit deformation field resulting from
  // the registration.
  if (argc > 9)
  {
 
    using VectorType = itk::Vector<float, ImageDimension>;
    using DisplacementFieldType = itk::Image<VectorType, ImageDimension>;
 
    auto field = DisplacementFieldType::New();
    field->SetRegions(fixedRegion);
    field->SetOrigin(fixedImage->GetOrigin());
    field->SetSpacing(fixedImage->GetSpacing());
    field->SetDirection(fixedImage->GetDirection());
    field->Allocate();
 
    using FieldIterator = itk::ImageRegionIterator<DisplacementFieldType>;
    FieldIterator fi(field, fixedRegion);
 
    fi.GoToBegin();
 
    DeformableTransformType::InputPointType  fixedPoint;
    DeformableTransformType::OutputPointType movingPoint;
    DisplacementFieldType::IndexType         index;
 
    VectorType displacement;
 
    while (!fi.IsAtEnd())
    {
      index = fi.GetIndex();
      field->TransformIndexToPhysicalPoint(index, fixedPoint);
      movingPoint = bsplineTransformFine->TransformPoint(fixedPoint);
      displacement = movingPoint - fixedPoint;
      fi.Set(displacement);
      ++fi;
    }
 
    using FieldWriterType = itk::ImageFileWriter<DisplacementFieldType>;
    auto fieldWriter = FieldWriterType::New();
 
    fieldWriter->SetInput(field);
 
    fieldWriter->SetFileName(argv[9]);
 
    std::cout << "Writing deformation field ...";
 
    try
    {
      fieldWriter->Update();
    }
    catch (const itk::ExceptionObject & excp)
    {
      std::cerr << "Exception thrown " << std::endl;
      std::cerr << excp << std::endl;
      return EXIT_FAILURE;
    }
 
    std::cout << " Done!" << std::endl;
  }
 
  // Optionally, save the transform parameters in a file
  if (argc > 6)
  {
    std::cout << "Writing transform parameter file ...";
    using TransformWriterType = itk::TransformFileWriter;
    auto transformWriter = TransformWriterType::New();
    transformWriter->AddTransform(bsplineTransformFine);
    transformWriter->SetFileName(argv[6]);
    transformWriter->Update();
    std::cout << " Done!" << std::endl;
  }
 
  return EXIT_SUCCESS;
}