Examples/RegistrationITKv3/DeformableRegistration6.cxx

/*=========================================================================
 *
 *  Copyright Insight Software Consortium
 *
 *  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
 *
 *         http://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 the use of the \doxygen{BSplineTransform}
// class in a manually controlled multi-resolution scheme. Here we define two
// transforms at two different resolution levels. A first registration is
// performed with the spline grid of low resolution, and the results are then
// used for initializing a higher resolution grid. Since this example is quite
// similar to the previous example on the use of the
// \code{BSplineTransform} we omit here most of the details already
// discussed and will focus on the aspects related to the multi-resolution
// approach.
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::LBFGSOptimizer}
//
//
// Software Guide : EndLatex

#include "itkImageRegistrationMethod.h"
#include "itkMeanSquaresImageToImageMetric.h"

//  Software Guide : BeginLatex
//
//  We include the header files for the transform and the optimizer.
//
//  \index{itk::BSplineTransform!header}
//  \index{itk::LBFGSOptimizer!header}
//
//  Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkBSplineTransform.h"
#include "itkLBFGSOptimizer.h"
// Software Guide : EndCodeSnippet


#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"

#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSquaredDifferenceImageFilter.h"

#include "itkBSplineResampleImageFunction.h"
#include "itkIdentityTransform.h"
#include "itkBSplineDecompositionImageFilter.h"

// NOTE: the LBFGSOptimizer does not invoke events


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 << " [deformationField] ";
    return EXIT_FAILURE;
    }

  constexpr unsigned int ImageDimension = 2;
  using PixelType = float;

  using FixedImageType = itk::Image< PixelType, ImageDimension >;
  using MovingImageType = itk::Image< PixelType, ImageDimension >;


  //  Software Guide : BeginLatex
  //
  //  We instantiate now the type of the \code{BSplineTransform} using
  //  as template parameters the type for coordinates representation, the
  //  dimension of the space, and the order of the BSpline.
  //
  //  \index{BSplineTransform!New}
  //  \index{BSplineTransform!Instantiation}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  const unsigned int SpaceDimension = ImageDimension;
  constexpr unsigned int SplineOrder = 3;
  using CoordinateRepType = double;

  using TransformType = itk::BSplineTransform<
                            CoordinateRepType,
                            SpaceDimension,
                            SplineOrder >;
  // Software Guide : EndCodeSnippet


  using OptimizerType = itk::LBFGSOptimizer;


  using MetricType = itk::MeanSquaresImageToImageMetric<
                                    FixedImageType,
                                    MovingImageType >;

  using InterpolatorType = itk:: LinearInterpolateImageFunction<
                                    MovingImageType,
                                    double          >;

  using RegistrationType = itk::ImageRegistrationMethod<
                                    FixedImageType,
                                    MovingImageType >;

  MetricType::Pointer         metric        = MetricType::New();
  OptimizerType::Pointer      optimizer     = OptimizerType::New();
  InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
  RegistrationType::Pointer   registration  = RegistrationType::New();


  registration->SetMetric(        metric        );
  registration->SetOptimizer(     optimizer     );
  registration->SetInterpolator(  interpolator  );


  //  Software Guide : BeginLatex
  //
  //  We construct two transform objects, each one will be configured for a resolution level.
  //  Notice than in this multi-resolution scheme we are not modifying the
  //  resolution of the image, but rather the flexibility of the deformable
  //  transform itself.
  //
  //  \index{itk::RegistrationMethod!SetTransform()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  TransformType::Pointer  transformLow = TransformType::New();
  registration->SetTransform( transformLow );
  // Software Guide : EndCodeSnippet

  using FixedImageReaderType = itk::ImageFileReader< FixedImageType  >;
  using MovingImageReaderType = itk::ImageFileReader< MovingImageType >;

  FixedImageReaderType::Pointer  fixedImageReader  = FixedImageReaderType::New();
  MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();

  fixedImageReader->SetFileName(  argv[1] );
  movingImageReader->SetFileName( argv[2] );

  FixedImageType::ConstPointer fixedImage = fixedImageReader->GetOutput();

  registration->SetFixedImage(  fixedImage   );
  registration->SetMovingImage(   movingImageReader->GetOutput()   );

  fixedImageReader->Update();

  FixedImageType::RegionType fixedRegion = fixedImage->GetBufferedRegion();

  registration->SetFixedImageRegion( fixedRegion );

  unsigned int numberOfGridNodes = 8;

  TransformType::PhysicalDimensionsType   fixedPhysicalDimensions;
  TransformType::MeshSizeType             meshSize;
  TransformType::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( numberOfGridNodes - SplineOrder );

  transformLow->SetTransformDomainOrigin( fixedOrigin );
  transformLow->SetTransformDomainPhysicalDimensions(
    fixedPhysicalDimensions );
  transformLow->SetTransformDomainMeshSize( meshSize );
  transformLow->SetTransformDomainDirection( fixedImage->GetDirection() );


  using ParametersType = TransformType::ParametersType;

  const unsigned int numberOfParameters =
               transformLow->GetNumberOfParameters();

  ParametersType parametersLow( numberOfParameters );

  parametersLow.Fill( 0.0 );

  transformLow->SetParameters( parametersLow );
  //  Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  We now pass the parameters of the current transform as the initial
  //  parameters to be used when the registration process starts.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  registration->SetInitialTransformParameters( transformLow->GetParameters() );


  optimizer->SetGradientConvergenceTolerance( 0.05 );
  optimizer->SetLineSearchAccuracy( 0.9 );
  optimizer->SetDefaultStepLength( 1.5 );
  optimizer->TraceOn();
  optimizer->SetMaximumNumberOfFunctionEvaluations( 1000 );
  std::cout << "Starting Registration with low resolution transform"
            << std::endl;

  try
    {
    registration->Update();
    std::cout << "Optimizer stop condition = "
              << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
    }
  catch( itk::ExceptionObject & err )
    {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
    }
  // Software Guide : EndCodeSnippet


  //  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

  TransformType::Pointer  transformHigh = TransformType::New();

  numberOfGridNodes = 12;

  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( numberOfGridNodes - SplineOrder );

  transformHigh->SetTransformDomainOrigin( fixedOrigin );
  transformHigh->SetTransformDomainPhysicalDimensions(
    fixedPhysicalDimensions );
  transformHigh->SetTransformDomainMeshSize( meshSize );
  transformHigh->SetTransformDomainDirection( fixedImage->GetDirection() );

  ParametersType parametersHigh( transformHigh->GetNumberOfParameters() );
  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 = TransformType::ImageType;
    using ResamplerType = itk::ResampleImageFilter<ParametersImageType,ParametersImageType>;
    ResamplerType::Pointer upsampler = ResamplerType::New();

    using FunctionType = itk::BSplineResampleImageFunction<ParametersImageType,double>;
    FunctionType::Pointer function = FunctionType::New();

    using IdentityTransformType = itk::IdentityTransform<double,SpaceDimension>;
    IdentityTransformType::Pointer identity = IdentityTransformType::New();

    upsampler->SetInput( transformLow->GetCoefficientImages()[k] );
    upsampler->SetInterpolator( function );
    upsampler->SetTransform( identity );
    upsampler->SetSize( transformHigh->GetCoefficientImages()[k]->
      GetLargestPossibleRegion().GetSize() );
    upsampler->SetOutputSpacing(
      transformHigh->GetCoefficientImages()[k]->GetSpacing() );
    upsampler->SetOutputOrigin(
      transformHigh->GetCoefficientImages()[k]->GetOrigin() );
    upsampler->SetOutputDirection( fixedImage->GetDirection() );

    using DecompositionType =
        itk::BSplineDecompositionImageFilter<ParametersImageType,ParametersImageType>;
    DecompositionType::Pointer decomposition = DecompositionType::New();

    decomposition->SetSplineOrder( SplineOrder );
    decomposition->SetInput( upsampler->GetOutput() );
    decomposition->Update();

    ParametersImageType::Pointer newCoefficients = decomposition->GetOutput();

    // copy the coefficients into the parameter array
    using Iterator = itk::ImageRegionIterator<ParametersImageType>;
    Iterator it( newCoefficients,
      transformHigh->GetCoefficientImages()[k]->GetLargestPossibleRegion() );
    while ( !it.IsAtEnd() )
      {
      parametersHigh[ counter++ ] = it.Get();
      ++it;
      }

    }

  transformHigh->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
  registration->SetInitialTransformParameters(transformHigh->GetParameters());
  registration->SetTransform( transformHigh );
  //  Software Guide : BeginLatex
  //
  //  Typically, we will also want to tighten the optimizer parameters
  //  when we move from lower to higher resolution grid.
  //
  //  Software Guide : EndLatex
  optimizer->SetGradientConvergenceTolerance( 0.01 );
  optimizer->SetDefaultStepLength( 0.25 );
  try
    {
    registration->Update();
    }
  catch( itk::ExceptionObject & err )
    {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
    }
  // Software Guide : EndCodeSnippet

  // Finally we use the last transform parameters in order to resample the image.
  //
  transformHigh->SetParameters( registration->GetLastTransformParameters() );

  using ResampleFilterType = itk::ResampleImageFilter<
                            MovingImageType,
                            FixedImageType >;

  ResampleFilterType::Pointer resample = ResampleFilterType::New();

  resample->SetTransform( transformHigh );
  resample->SetInput( movingImageReader->GetOutput() );

  resample->SetSize(    fixedImage->GetLargestPossibleRegion().GetSize() );
  resample->SetOutputOrigin(  fixedImage->GetOrigin() );
  resample->SetOutputSpacing( fixedImage->GetSpacing() );
  resample->SetOutputDirection( fixedImage->GetDirection() );
  resample->SetDefaultPixelValue( 100 );

  using OutputPixelType = unsigned char;

  using OutputImageType = itk::Image< OutputPixelType, ImageDimension >;

  using CastFilterType = itk::CastImageFilter<
                        FixedImageType,
                        OutputImageType >;

  using WriterType = itk::ImageFileWriter< OutputImageType >;


  WriterType::Pointer      writer =  WriterType::New();
  CastFilterType::Pointer  caster =  CastFilterType::New();


  writer->SetFileName( argv[3] );


  caster->SetInput( resample->GetOutput() );
  writer->SetInput( caster->GetOutput()   );


  try
    {
    writer->Update();
    }
  catch( itk::ExceptionObject & err )
    {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
    }

  using DifferenceFilterType = itk::SquaredDifferenceImageFilter<
                                  FixedImageType,
                                  FixedImageType,
                                  OutputImageType >;

  DifferenceFilterType::Pointer difference = DifferenceFilterType::New();

  WriterType::Pointer writer2 = WriterType::New();
  writer2->SetInput( difference->GetOutput() );


  // Compute the difference image between the
  // fixed and resampled moving image.
  if( argc >= 5 )
    {
    difference->SetInput1( fixedImageReader->GetOutput() );
    difference->SetInput2( resample->GetOutput() );
    writer2->SetFileName( argv[4] );
    try
      {
      writer2->Update();
      }
    catch( itk::ExceptionObject & err )
      {
      std::cerr << "ExceptionObject caught !" << std::endl;
      std::cerr << err << std::endl;
      return EXIT_FAILURE;
      }
    }


  // Compute the difference image between the
  // fixed and moving image before registration.
  if( argc >= 6 )
    {
    writer2->SetFileName( argv[5] );
    difference->SetInput1( fixedImageReader->GetOutput() );
    difference->SetInput2( movingImageReader->GetOutput() );
    try
      {
      writer2->Update();
      }
    catch( itk::ExceptionObject & err )
      {
      std::cerr << "ExceptionObject caught !" << std::endl;
      std::cerr << err << std::endl;
      return EXIT_FAILURE;
      }
    }

  // Generate the explicit deformation field resulting from
  // the registration.

  using VectorType = itk::Vector< float, ImageDimension >;
  using DisplacementFieldType = itk::Image< VectorType, ImageDimension >;

  DisplacementFieldType::Pointer 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();

  TransformType::InputPointType  fixedPoint;
  TransformType::OutputPointType movingPoint;
  DisplacementFieldType::IndexType index;

  VectorType displacement;

  while( ! fi.IsAtEnd() )
    {
    index = fi.GetIndex();
    field->TransformIndexToPhysicalPoint( index, fixedPoint );
    movingPoint = transformHigh->TransformPoint( fixedPoint );
    displacement = movingPoint - fixedPoint;
    fi.Set( displacement );
    ++fi;
    }

  using FieldWriterType = itk::ImageFileWriter< DisplacementFieldType >;
  FieldWriterType::Pointer fieldWriter = FieldWriterType::New();

  fieldWriter->SetInput( field );

  if( argc >= 7 )
    {
    fieldWriter->SetFileName( argv[6] );
    try
      {
      fieldWriter->Update();
      }
    catch( itk::ExceptionObject & excp )
      {
      std::cerr << "Exception thrown " << std::endl;
      std::cerr << excp << std::endl;
      return EXIT_FAILURE;
      }
    }

  return EXIT_SUCCESS;
}