FSL diffusion preprocessings

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# The full use case code: caps.quality_control.snapshots.pilot
from caps.toy_datasets import get_sample_data
from capsul.study_config import StudyConfig
from capsul.utils.sorted_dictionary import SortedDictionary
from caps.dicom_converter.base.tools import ensure_is_dir
toy_dataset = get_sample_data("mni_2mm")
print(toy_dataset.mni, toy_dataset.mask)
snap_pipeline = Snap()
print(snap_pipeline.get_input_spec())
snap_pipeline.switch_QC = "MULTI"

snap_pipeline.input_image = toy_dataset.mni
snap_pipeline.lower_bound = 0.15
snap_pipeline.upper_bound = 0.85
snap_pipeline.nb_steps = 6
snap_pipeline.edges_image = toy_dataset.mask
"""
Study Configuration
-------------------

The pipeline is now set up and ready to be executed.
For a complete description of a study execution, see the
:ref:`Study Configuration description <study_configuration_guide>`    snap_working_dir = os.path.join(working_dir, "snap")
ensure_is_dir(snap_working_dir)
default_config = SortedDictionary(
    ("output_directory", snap_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(snap_pipeline)
print "\nOUTPUTS\n"
for trait_name, trait_value in \
                    snap_pipeline.get_outputs().iteritems():
    print "{0}: {1}".format(trait_name, trait_value)

Objective

We propose to make a quality control of a diffusion sequence: Simple snapshot (SCA) Multi snapshot, more complete (MULTI)

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 caps.dicom_converter.base.tools import ensure_is_dir

Load the toy dataset

We want to perform Snap 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("mni_2mm")

The toy_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(toy_dataset.mni, toy_dataset.mask)

Will return:

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

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 the diffusion preprocessings.

snap_pipeline = Snap()

It is possible to access the pipeline input specification.

print(snap_pipeline.get_input_spec())

Will return the input parameters the user can set:

INPUT SPECIFICATIONS

switch_mode: ['Enum']
lower_bound: ['Float']
upper_bound: ['Float']
nb_steps: ['Int']
output_dir: ['Directory']
target: ['File']
edges_image: ['File']
input_image: ['File']

We activate the multi snap path

snap_pipeline.switch_QC = "MULTI"

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

snap_pipeline.input_image = toy_dataset.mni
snap_pipeline.lower_bound = 0.15
snap_pipeline.upper_bound = 0.85
snap_pipeline.nb_steps = 6
snap_pipeline.edges_image = toy_dataset.mask
"""
Study Configuration
-------------------

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

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

snap_working_dir = os.path.join(working_dir, "snap")
ensure_is_dir(snap_working_dir)
default_config = SortedDictionary(
    ("output_directory", snap_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(snap_pipeline)

Results

Finally, we print the pipeline outputs

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

Note

Since only the motion and eddy corrections has been selected, the unwrapped_phase_file and susceptibility_corrected_file are not specified. Thus the corrected_file output contains the motion-eddy corrected image.