Diffusion Brain Extraction Tool

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# The full use case code: caps.diffusion_preproc.bet.pilot
from caps.toy_datasets import get_sample_data
from capsul.study_config import StudyConfig
from capsul.utils.sorted_dictionary import SortedDictionary
from nsap.lib.base import ensure_is_dir
toy_dataset = get_sample_data("dwi")
print(toy_dataset.dwi, toy_dataset.bvals)
bet_pipeline = dBET()
print(bet_pipeline.get_input_spec())
bet_pipeline.dw_image = toy_dataset.dwi
bet_pipeline.bvals = toy_dataset.bvals
bet_working_dir = os.path.join(working_dir, "diffusion_bet")
ensure_is_dir(bet_working_dir)
default_config = SortedDictionary(
    ("output_directory", bet_working_dir),
    ("fsl_config", "/etc/fsl/4.1/fsl.sh"),
    ("use_fsl", True),
    ("use_smart_caching", True),
    ("generate_logging", True)
)
study = StudyConfig(default_config)
study.run(bet_pipeline)
print("\nOUTPUTS\n")
for trait_name, trait_value in bet_pipeline.get_outputs().iteritems():
    print("{0}: {1}".format(trait_name, trait_value))

Objective

We propose to extract the brain mask from a diffusion sequence.

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

Load the toy dataset

We want to perform BET on a diffusion sequence. To do so, we use the get_sample_data function to load this dataset.

See also

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

toy_dataset = get_sample_data("dwi")

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

print(toy_dataset.dwi, toy_dataset.bvals)

Will return:

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

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

Processing definition

Now we need to define the processing step that will perform BET on diffusion sequence.

bet_pipeline = dBET()

It is possible to access the ipeline input specification.

print(bet_pipeline.get_input_spec())

Will return the input parameters the user can set:

INPUT SPECIFICATIONS

dw_image: ['File']
bvals: ['File']
specified_index_of_ref_image: ['Int']
terminal_output: ['Enum']
generate_binary_mask: ['Bool']
use_4d_input: ['Bool']
generate_mesh: ['Bool']
generate_skull: ['Bool']
bet_threshold: ['Float']

We can now tune the pipeline parameters. We first set the input dwi file:

bet_pipeline.dw_image = toy_dataset.dwi

And set the b-values file

bet_pipeline.bvals = toy_dataset.bvals

Study Configuration

The pipeline is now set up and ready to be executed. For a complete description of a study execution, see the Study Configuration description

bet_working_dir = os.path.join(working_dir, "diffusion_bet")
ensure_is_dir(bet_working_dir)
default_config = SortedDictionary(
    ("output_directory", bet_working_dir),
    ("fsl_config", "/etc/fsl/4.1/fsl.sh"),
    ("use_fsl", True),
    ("use_smart_caching", True),
    ("generate_logging", True)
)
study = StudyConfig(default_config)
study.run(bet_pipeline)

Results

Finally, we print the pipeline outputs

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

Note

Since only the brain mask has been requested, all the other outputs are set to None. Only the bet_out_file, splited_images, bet_mask_file, ref_image, index_of_ref_image outputs are of interest for this study.