FSL FA Registation¶

Objective

We propose here to register a fractional anisotropy image to a reference template and to apply the resulting deformation field to the tensor image.

Import¶

First we load the function that enables us to access the toy datasets

from caps.toy_datasets import get_sample_data

From capsul we then load the class to configure the study we want to perform

from capsul.study_config import StudyConfig

Here two utility tools are loaded. The first one enables the creation of ordered dictionary and the second ensure that a directory exist. Note that the directory will be created if necessary.

from capsul.utils.sorted_dictionary import SortedDictionary
from nsap.lib.base import ensure_is_dir

We need some generic python modules

import os

From caps we need the pipeline that will enable use to register the tensor field.

from caps.diffusion_registration.fsl_registration import FSLRegistration

Study Configuration¶

For a complete description of a study configuration, see the Study Configuration description

We first define the current working directory

working_dir = "/volatile/nsap/caps"
registration_working_dir = os.path.join(working_dir,
                                        "diffusion_registration")
ensure_is_dir(registration_working_dir)

And then define the study configuration.

default_config = SortedDictionary(
    ("output_directory", registration_working_dir),
    ("fsl_config", "/etc/fsl/4.1/fsl.sh"),
    ("use_fsl", True),
    ("use_smart_caching", True),
    ("generate_logging", True)
)
study = StudyConfig(default_config)

Load the toy dataset¶

We want to perform a second order tensor fit on a diffusion sequence data. To do so, we use the get_sample_data function to load the diffusion dataset and the taget template.

See also

For a complete description of the get_sample_data function, see the Toy Datasets documentation

diffusion_dataset = get_sample_data("dwi")
target_dataset = get_sample_data("fa_1mm")

The diffusion_dataset is an Enum structure with some specific elements of interest dwi, bvals, bvecs that contain the nifti diffusion image ,the b-values and the b-vectors respectively.

print(diffusion_dataset.dwi, diffusion_dataset.bvals,
      diffusion_dataset.bvecs)

Will return:

/home/ag239446/git/nsap-src/nsap/data/DTI30s010.nii
/home/ag239446/git/nsap-src/nsap/data/DTI30s010.bval
/home/ag239446/git/nsap-src/nsap/data/DTI30s010.bvec

We can see that the image has been found in a local directory

The target_dataset is an Enum structure with some specific elements of interest template, nl_config that contain the fa diffusion template and the fsl fnirt configuration file.

print(target_dataset.template, target_dataset.nl_config)

Will return:

/usr/share/fsl/4.1/data/standard/FMRIB58_FA_1mm.nii.gz
/usr/share/fsl/4.1/etc/flirtsch/FA_2_FMRIB58_1mm.cnf

We can see that the information has been found in the fsl directory.

Processing definition¶

Now we need to define the processing steps that will perform the tensor registration.

registration_pipeline = FSLRegistration()

It is possible to access the pipeline input specification.

print(registration_pipeline.get_input_spec())

Will return the input parameters the user can set:

INPUT SPECIFICATIONS

fa_file: ['File']
target_file: ['File']
mask_file: ['File']
config_file: ['Enum', 'File']

We can now tune the pipeline parameters.

registration_pipeline.fa_file = diffusion_dataset.fa
registration_pipeline.target_file = target_dataset.template
registration_pipeline.mask_file = diffusion_dataset.mask
registration_pipeline.config_file = target_dataset.nl_config
registration_pipeline.tensor_file = diffusion_dataset.tensor

The pipeline is now ready to be run

study.run(registration_pipeline)

Results¶

Finally, we print the pipeline outputs

print("\nOUTPUTS\n")
outputs = registration_pipeline.get_outputs()
for trait_name, trait_value in outputs.iteritems():
    print("{0}: {1}".format(trait_name, trait_value))

Will return:

OUTPUTS