Functional Spatial Preprocessings¶

Objective

We propose here to spatialy correct functional images using PyPreProcess (SPM).

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 exists. 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
import numpy

From caps we need the pipeline that will enable use to do the functional spatial preprocessings:

from caps.functional_preproc.pypreprocess_fmri_preproc import (
    SPMSubjectPreprocessing)

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"
preproc_working_dir = os.path.join(working_dir, "functional_preroc")
ensure_is_dir(preproc_working_dir)

And then define the study configuration:

default_config = SortedDictionary(
    ("output_directory", preproc_working_dir),
    ("spm_directory", "/i2bm/local/spm8-5236"),
    ("matlab_exec", "/neurospin/local/bin/matlab"),
    ("spm_exec_cmd", "/i2bm/local/bin/spm8"),
    ("use_spm_mcr", False),
    ("use_smart_caching", False),
    ("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 dataset:

Processing definition¶

Now we need to define the processing step that will perform the functional spatial preprocessings:

preproc_pipeline = SPMSubjectPreprocessing()

It is possible to access the pipeline input specifications:

print(preproc_pipeline.get_input_spec())

Will return the input parameters the user can set:

INPUT SPECIFICATIONS

func_file: ['File']
anat_file: ['File']
output_directory: ['Directory']
slice_time: ['Bool']
realign: ['Bool']
coregister: ['Bool']
coreg_anat_to_func: ['Bool']
segment: ['Bool']
normalize: ['Bool']
fwhm: ['List_Float', 'Float', 'TraitInstance']
repetition_tile: ['Float']
slice_order: ['List_Int']

We can now tune the pipeline parameters:

preproc_pipeline.func_file = toy_dataset.fmri
preproc_pipeline.anat_file = toy_dataset.anat
preproc_pipeline.repetition_time = toy_dataset.TR
preproc_pipeline.slice_order = timings
preproc_pipeline.fwhm = [5, 5, 5]
preproc_pipeline.coreg_anat_to_func = False

Before running the pipeline, you need to select the spatial preproc that will be performed:

preproc_pipeline.slice_time = True
preproc_pipeline.realign = True
preproc_pipeline.coregister = True
preproc_pipeline.segment = True
preproc_pipeline.normalize = True

You can also specify that you want to activate nipype smart-caching

preproc_pipeline.use_smart_caching = True

The pipeline is now ready to be run

study.run(preproc_pipeline)

Results¶

Finally, we print the pipeline outputs

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