ITK  4.3.0
Insight Segmentation and Registration Toolkit
Examples/Filtering/DiffusionTensor3DReconstructionImageFilter.cxx

Sample DTI datasets may be obtained from

*   \li ftp://public.kitware.com/pub/namic/DTI/Data/dwi.nhdr
*   \li ftp://public.kitware.com/pub/namic/DTI/Data/dwi.img.gz ( gunzip this )
* 
See Also
DiffusionTensor3D SymmetricSecondRankTensor
/*=========================================================================
*
* 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.
*
*=========================================================================*/
//
// This example shows how to use the DiffusionTensor3DReconstructionImageFilter
// to reconstruct an image of tensors from Diffusion weighted images. See the
// documentation of DiffusionTensor3DReconstructionImageFilter,
// TensorRelativeAnisotropyImageFilter, TensorFractionalAnisotropyImageFilter
// first.
//
// The example takes diffusion weighted images in the Nrrd format, writes out
// the gradient and reference images for the users reference and computes and
// writes out the Fractional Anisotropy and Relative Anisotropy images.
//
// The easiest way to get started is to try out the filter on a sample dataset.
//
// Acquiring sample datasets:
// 1. Get the DWI datasets from
// ftp://public.kitware.com/pub/namic/DTI/Data/dwi.nhdr
// ftp://public.kitware.com/pub/namic/DTI/Data/dwi.img.gz (gunzip this)
// These datasets contain a reference T1 image and 30 diffusion weighted
// images. See the nrrd header for details such as B value etc..
//
// 2. Run the example with the following args
// dwi.nhdr 80 Tensors.mhd FractionalAnisotropy.mhd RelativeAnisotropy.mhd 1
//
// 3. You should find 30 gradient images, 1 reference image, the FA and RA images
// in your working directory, which you can fire up in your favourite volume
// browser.
//
// This work is part of the National Alliance for Medical Image
// Computing (NAMIC), funded by the National Institutes of Health
// through the NIH Roadmap for Medical Research, Grant U54 EB005149.
//
// Additional documentation: For details on the Nrrd format for DTI, see
// http://wiki.na-mic.org/Wiki/index.php/NAMIC_Wiki:DTI:Nrrd_format
//
#include "itkNrrdImageIO.h"
#include <iostream>
int main( int argc, char *argv[] )
{
if(argc < 3)
{
std::cerr << "Usage: " << argv[0] << " NrrdFileName(.nhdr) threshold(on B0)"
<< " FAImageFileName RelativeAnisotropyFileName " <<
"[ExtractGradientAndReferenceImage from the NRRD file and "
<< "write them as images]" << std::endl;
std::cerr << "\tExample args: xt_dwi.nhdr 80 FA.mhd 1" << std::endl;
return EXIT_FAILURE;
}
const unsigned int Dimension = 3;
unsigned int numberOfImages = 0;
unsigned int numberOfGradientImages = 0;
bool readb0 = false;
double b0 = 0;
typedef unsigned short PixelType;
ImageType::Pointer img;
// Set the properties for NrrdReader
reader->SetFileName(argv[1]);
// Read in the nrrd data. The file contains the reference image and the gradient
// images.
try
{
reader->Update();
img = reader->GetOutput();
}
{
std::cout << ex << std::endl;
return EXIT_FAILURE;
}
// Here we instantiate the DiffusionTensor3DReconstructionImageFilter class.
// The class is templated over the pixel types of the reference, gradient
// and the to be created tensor pixel's precision. (We use double here). It
// takes as input the Reference (B0 image aquired in the absence of diffusion
// sensitizing gradients), 'n' Gradient images and their directions and produces
// as output an image of tensors with pixel-type DiffusionTensor3D.
//
PixelType, PixelType, double > TensorReconstructionImageFilterType;
// -------------------------------------------------------------------------
// Parse the Nrrd headers to get the B value and the gradient directions used
// for diffusion weighting.
//
// The Nrrd headers should look like :
// The tags specify the B value and the gradient directions. If gradient
// directions are (0,0,0), it indicates that it is a reference image.
//
// DWMRI_b-value:=800
// DWMRI_gradient_0000:= 0 0 0
// DWMRI_gradient_0001:=-1.000000 0.000000 0.000000
// DWMRI_gradient_0002:=-0.166000 0.986000 0.000000
// DWMRI_gradient_0003:=0.110000 0.664000 0.740000
// ...
//
itk::MetaDataDictionary imgMetaDictionary = img->GetMetaDataDictionary();
std::vector<std::string> imgMetaKeys = imgMetaDictionary.GetKeys();
std::vector<std::string>::const_iterator itKey = imgMetaKeys.begin();
std::string metaString;
TensorReconstructionImageFilterType::GradientDirectionType vect3d;
TensorReconstructionImageFilterType::GradientDirectionContainerType::Pointer
DiffusionVectors =
TensorReconstructionImageFilterType::GradientDirectionContainerType::New();
for (; itKey != imgMetaKeys.end(); ++itKey)
{
double x,y,z;
itk::ExposeMetaData<std::string> (imgMetaDictionary, *itKey, metaString);
if (itKey->find("DWMRI_gradient") != std::string::npos)
{
std::cout << *itKey << " ---> " << metaString << std::endl;
sscanf(metaString.c_str(), "%lf %lf %lf\n", &x, &y, &z);
vect3d[0] = x; vect3d[1] = y; vect3d[2] = z;
DiffusionVectors->InsertElement( numberOfImages, vect3d );
++numberOfImages;
// If the direction is 0.0, this is a reference image
if (vect3d[0] == 0.0 &&
vect3d[1] == 0.0 &&
vect3d[2] == 0.0)
{
continue;
}
++numberOfGradientImages;
}
else if (itKey->find("DWMRI_b-value") != std::string::npos)
{
std::cout << *itKey << " ---> " << metaString << std::endl;
readb0 = true;
b0 = atof(metaString.c_str());
}
}
std::cout << "Number of gradient images: "
<< numberOfGradientImages
<< " and Number of reference images: "
<< numberOfImages - numberOfGradientImages
<< std::endl;
if(!readb0)
{
std::cerr << "BValue not specified in header file" << std::endl;
return EXIT_FAILURE;
}
// -------------------------------------------------------------------------
// Extract the Reference and gradient images from the NRRD file
// as seperate images.
//
// This is not really necessary, the filter is capable of gobbling the entire
// VectorImage (which contains the reference and the gradient image) and chew
// on it to generate the TensorImage. This is a more intuitive formalism
// since this is what is usually obtained from the Nrrd DWI format.
//
// Nevertheless, we go through the "unnecessary pain" of extracting the
// gradient and reference images in separate images and writing them out to
// files, so they can be fired up in you favourite volume viewer.
//
typedef itk::Image< PixelType, Dimension > ReferenceImageType;
typedef ReferenceImageType GradientImageType;
// A container of smart pointers to images. This container will hold
// the 'numberOfGradientImages' gradient images and the reference image.
//
std::vector< GradientImageType::Pointer > imageContainer;
// iterator to iterate over the DWI Vector image just read in.
DWIIteratorType dwiit( img, img->GetBufferedRegion() );
// In this for loop, we will extract the 'n' gradient images + 1 reference
// image from the DWI Vector image.
//
for( unsigned int i = 0; i<numberOfImages; i++ )
{
GradientImageType::Pointer image = GradientImageType::New();
image->CopyInformation( img );
image->SetBufferedRegion( img->GetBufferedRegion() );
image->SetRequestedRegion( img->GetRequestedRegion() );
image->Allocate();
IteratorType it( image, image->GetBufferedRegion() );
dwiit.GoToBegin();
it.GoToBegin();
while (!it.IsAtEnd())
{
it.Set(dwiit.Get()[i]);
++it;
++dwiit;
}
imageContainer.push_back( image );
}
// If we need to write out the reference and gradient images to a file..
// Easier viewing them with a viewer than if they were in a single NRRD
// file
if( (argc > 4) && (atoi(argv[6])) )
{
unsigned int referenceImageIndex = 0;
typedef itk::ImageFileWriter< GradientImageType > GradientWriterType;
for( unsigned int i = 0; i<numberOfImages; i++ )
{
GradientWriterType::Pointer gradientWriter = GradientWriterType::New();
gradientWriter->SetInput( imageContainer[i] );
char filename[100];
if (DiffusionVectors->ElementAt(i).two_norm() <= 0.0) // this is a reference image
{
std::string fn("ReferenceImage%d.mhd");
sprintf(filename, fn.c_str(), referenceImageIndex );
++referenceImageIndex;
}
else
{
std::string fn("Gradient%d.mhd");
sprintf(filename, fn.c_str(), i );
}
gradientWriter->SetFileName( filename );
gradientWriter->Update();
}
}
// -------------------------------------------------------------------------
TensorReconstructionImageFilterType::Pointer tensorReconstructionFilter =
TensorReconstructionImageFilterType::New();
// The reference and the gradient images are conveniently provided as
// input to the DiffusionTensor3DReconstructionImageFilter as
// filter->SetGradientImage( directionsContainer, nrrdreader->GetOutput() );
//
// The output of the nrrdreader is a VectorImage (akin to a multicomponent
// 3D image). The nth component image is treated as a reference image if its
// corresponding gradient direction is (0,0,0). Any number of reference images
// may be specified.
//
// An alternate way to provide the inputs, when you have the reference and
// gradient images in seperate itk::Image< type, 3 > is :
//
// tensorReconstructionFilter->SetReferenceImage( image0 );
// tensorReconstructionFilter->AddGradientImage( direction1, image1 );
// tensorReconstructionFilter->AddGradientImage( direction2, image2 );
//
tensorReconstructionFilter->SetGradientImage( DiffusionVectors, reader->GetOutput() );
// This is necessary until we fix netlib/dsvdc.c
tensorReconstructionFilter->SetNumberOfThreads( 1 );
tensorReconstructionFilter->SetBValue(b0);
tensorReconstructionFilter->SetThreshold( static_cast<
TensorReconstructionImageFilterType::ReferencePixelType >(
atof(argv[2])));
tensorReconstructionFilter->Update();
// -------------------------------------------------------------------------
// Write out the image of tensors. This code snippet goes to show that you
// can use itk::ImageFileWriter to write an image of tensors.
//
TensorReconstructionImageFilterType::OutputImageType > TensorWriterType;
TensorWriterType::Pointer tensorWriter = TensorWriterType::New();
tensorWriter->SetFileName( argv[3] );
tensorWriter->SetInput( tensorReconstructionFilter->GetOutput() );
tensorWriter->Update();
// -------------------------------------------------------------------------
// Now that we have the image of tensors, we may use one of the many tensor
// filters in ITK. Below, we use the TensorFractionalAnisotropyImageFilter to
// compute the FA.
//
typedef TensorReconstructionImageFilterType::TensorPixelType TensorPixelType;
typedef TensorPixelType::RealValueType RealValueType;
TensorReconstructionImageFilterType::OutputImageType, FAImageType >
FAFilterType;
FAFilterType::Pointer fractionalAnisotropyFilter = FAFilterType::New();
fractionalAnisotropyFilter->SetInput( tensorReconstructionFilter->GetOutput() );
// Write the FA image
//
FAWriterType::Pointer faWriter = FAWriterType::New();
faWriter->SetInput( fractionalAnisotropyFilter->GetOutput() );
faWriter->SetFileName(argv[4]);
faWriter->Update();
// Compute and write the Relative Anisotropy
//
typedef TensorReconstructionImageFilterType::TensorPixelType TensorPixelType;
typedef TensorPixelType::RealValueType RealValueType;
TensorReconstructionImageFilterType::OutputImageType, RAImageType >
RAFilterType;
RAFilterType::Pointer relativeAnisotropyFilter = RAFilterType::New();
relativeAnisotropyFilter->SetInput( tensorReconstructionFilter->GetOutput() );
RAWriterType::Pointer raWriter = RAWriterType::New();
raWriter->SetInput( relativeAnisotropyFilter->GetOutput() );
raWriter->SetFileName(argv[5]);
raWriter->Update();
return EXIT_SUCCESS;
}