def test_cache_download(self):
repository = XnatRepository(project_id=self.project,
server=SERVER,
cache_dir=tempfile.mkdtemp())
study = self.create_study(TestStudy,
'cache_download',
inputs=[
FilesetSelector('fileset1', text_format,
'fileset1'),
FilesetSelector('fileset3', text_format,
'fileset3')
],
repository=repository)
study.cache_inputs()
for subject_id, visits in list(self.STRUCTURE.items()):
subj_dir = op.join(repository.cache_dir, self.project,
'{}_{}'.format(self.project, subject_id))
for visit_id in visits:
sess_dir = op.join(
subj_dir, '{}_{}_{}'.format(self.project, subject_id,
visit_id))
for inpt in study.inputs:
self.assertTrue(
op.exists(
op.join(sess_dir,
inpt.name + inpt.format.extension)))
def test_preprocess(self):
study = self.create_study(DiffusionStudy, 'preprocess', [
FilesetSelector('magnitude', 'r_l_dwi_b700_30', mrtrix_format),
FilesetSelector('dwi_reference', 'l_r_dwi_b0_6', mrtrix_format)
])
preproc = list(study.data('preproc'))[0]
self.assertTrue(os.path.exists(preproc.path))
def test_full_multi_study(self):
study = self.create_study(
FullMultiStudy,
'full', [
FilesetSelector('a', text_format, 'ones'),
FilesetSelector('b', text_format, 'ones'),
FilesetSelector('c', text_format, 'ones')
],
parameters=[Parameter('required_op', 'mul')])
d, e, f = study.data(('d', 'e', 'f'),
subject_id='SUBJECT',
visit_id='VISIT')
self.assertContentsEqual(d, 2.0)
self.assertContentsEqual(e, 3.0)
self.assertContentsEqual(f, 6.0)
# Test parameter values in MultiStudy
self.assertEqual(study._get_parameter('p1').value, 100)
self.assertEqual(study._get_parameter('p2').value, '200')
self.assertEqual(study._get_parameter('p3').value, 300.0)
self.assertEqual(study._get_parameter('q1').value, 150)
self.assertEqual(study._get_parameter('q2').value, '250')
self.assertEqual(study._get_parameter('required_op').value, 'mul')
# Test parameter values in SubStudy
ss1 = study.sub_study('ss1')
self.assertEqual(ss1._get_parameter('o1').value, 100)
self.assertEqual(ss1._get_parameter('o2').value, '200')
self.assertEqual(ss1._get_parameter('o3').value, 300.0)
ss2 = study.sub_study('ss2')
self.assertEqual(ss2._get_parameter('o1').value, 150)
self.assertEqual(ss2._get_parameter('o2').value, '250')
self.assertEqual(ss2._get_parameter('o3').value, 300.0)
self.assertEqual(ss2._get_parameter('product_op').value, 'mul')
def test_partial_multi_study(self):
study = self.create_study(
PartialMultiStudy,
'partial', [
FilesetSelector('a', text_format, 'ones'),
FilesetSelector('b', text_format, 'ones'),
FilesetSelector('c', text_format, 'ones')
],
parameters=[Parameter('ss2_product_op', 'mul')])
ss1_z = study.data('ss1_z', subject_id='SUBJECT', visit_id='VISIT')
ss2_z = list(study.data('ss2_z'))[0]
self.assertContentsEqual(ss1_z, 2.0)
self.assertContentsEqual(study.data('ss2_y'), 3.0)
self.assertContentsEqual(ss2_z, 6.0)
# Test parameter values in MultiStudy
self.assertEqual(study._get_parameter('p1').value, 1000)
self.assertEqual(study._get_parameter('ss1_o2').value, '2')
self.assertEqual(study._get_parameter('ss1_o3').value, 3.0)
self.assertEqual(study._get_parameter('ss2_o2').value, '20')
self.assertEqual(study._get_parameter('ss2_o3').value, 30.0)
self.assertEqual(study._get_parameter('ss2_product_op').value, 'mul')
# Test parameter values in SubStudy
ss1 = study.sub_study('ss1')
self.assertEqual(ss1._get_parameter('o1').value, 1000)
self.assertEqual(ss1._get_parameter('o2').value, '2')
self.assertEqual(ss1._get_parameter('o3').value, 3.0)
ss2 = study.sub_study('ss2')
self.assertEqual(ss2._get_parameter('o1').value, 1000)
self.assertEqual(ss2._get_parameter('o2').value, '20')
self.assertEqual(ss2._get_parameter('o3').value, 30.0)
self.assertEqual(ss2._get_parameter('product_op').value, 'mul')
def test_extract_b0(self):
study = self.create_study(DiffusionStudy, 'extract_b0', [
FilesetSelector('preproc', 'preproc', nifti_gz_format),
FilesetSelector('grad_dirs', 'gradient_dirs', fsl_bvecs_format),
FilesetSelector('bvalues', 'bvalues', fsl_bvals_format)
])
study.extract_b0_pipeline().run(work_dir=self.work_dir)
self.assertFilesetCreated('primary.nii.gz', study.name)
def test_repository_roundtrip(self):
# Create working dirs
# Create DarisSource node
repository = XnatRepository(project_id=self.project,
server=SERVER,
cache_dir=self.cache_dir)
study = DummyStudy(self.STUDY_NAME,
repository,
processor=LinearProcessor('a_dir'),
inputs=[
FilesetSelector('source1', text_format,
'source1'),
FilesetSelector('source2', text_format,
'source2'),
FilesetSelector('source3', text_format,
'source3'),
FilesetSelector('source4', text_format,
'source4')
])
# TODO: Should test out other file formats as well.
source_files = ['source1', 'source2', 'source3', 'source4']
sink_files = ['sink1', 'sink3', 'sink4']
inputnode = pe.Node(IdentityInterface(['subject_id', 'visit_id']),
'inputnode')
inputnode.inputs.subject_id = str(self.SUBJECT)
inputnode.inputs.visit_id = str(self.VISIT)
source = study.source(source_files)
sink = study.sink(sink_files)
sink.inputs.name = 'repository-roundtrip-unittest'
sink.inputs.desc = (
"A test session created by repository roundtrip unittest")
# Create workflow connecting them together
workflow = pe.Workflow('source-sink-unit-test', base_dir=self.work_dir)
workflow.add_nodes((source, sink))
workflow.connect(inputnode, 'subject_id', source, 'subject_id')
workflow.connect(inputnode, 'visit_id', source, 'visit_id')
workflow.connect(inputnode, 'subject_id', sink, 'subject_id')
workflow.connect(inputnode, 'visit_id', sink, 'visit_id')
for source_name in source_files:
if source_name != 'source2':
sink_name = source_name.replace('source', 'sink')
workflow.connect(source, source_name + PATH_SUFFIX, sink,
sink_name + PATH_SUFFIX)
workflow.run()
# Check cache was created properly
self.assertEqual(
ls_with_md5_filter(self.session_cache()),
['source1.txt', 'source2.txt', 'source3.txt', 'source4.txt'])
expected_sink_filesets = ['sink1', 'sink3', 'sink4']
self.assertEqual(
ls_with_md5_filter(self.session_cache(from_study=self.STUDY_NAME)),
[d + text_format.extension for d in expected_sink_filesets])
with self._connect() as login:
fileset_names = list(login.experiments[self.session_label(
from_study=self.STUDY_NAME)].scans.keys())
self.assertEqual(sorted(fileset_names), expected_sink_filesets)
def test_response(self):
study = self.create_study(DiffusionStudy, 'response', [
FilesetSelector('bias_correct', 'bias_correct', nifti_gz_format),
FilesetSelector('brain_mask', 'brain_mask', nifti_gz_format),
FilesetSelector('grad_dirs', 'gradient_dirs', fsl_bvecs_format),
FilesetSelector('bvalues', 'bvalues', fsl_bvals_format)
])
study.response_pipeline().run(work_dir=self.work_dir)
self.assertFilesetCreated('response.txt', study.name)
def test_bias_correct(self):
study = self.create_study(DiffusionStudy, 'bias_correct', [
FilesetSelector('preproc', 'preproc', nifti_gz_format),
FilesetSelector('grad_dirs', 'gradient_dirs', fsl_bvecs_format),
FilesetSelector('bvalues', 'bvalues', fsl_bvals_format)
])
study.bias_correct_pipeline(mask_tool='mrtrix').run(
work_dir=self.work_dir)
self.assertFilesetCreated('bias_correct.nii.gz', study.name)
def test_registration_to_matrix(self):
study = self.create_study(
CoregisteredToMatrixStudy, 'registration_to_matrix', {
FilesetSelector('to_register', 'flair', nifti_gz_format),
FilesetSelector('reference', 'mprage', nifti_gz_format),
FilesetSelector('matrix', 'matrix', text_matrix_format)
})
study.linear_registration_pipeline().run(work_dir=self.work_dir)
self.assertFilesetCreated('registered.nii.gz', study.name)
def test_input_validation(self):
self.create_study(TestInputValidationStudy,
'test_input_validation',
inputs=[
FilesetSelector('a', test1_format, 'a'),
FilesetSelector('b', test3_format, 'b'),
FilesetSelector('c', test1_format, 'a'),
FilesetSelector('d', test3_format, 'd')
])
def test_input_validation_fail(self):
self.assertRaises(ArcanaUsageError,
self.create_study,
TestInputValidationStudy,
'test_validation_fail',
inputs=[
FilesetSelector('a', test3_format, 'a'),
FilesetSelector('b', test3_format, 'b')
])
def test_ute(self):
study = self.create_study(
UTEStudy, 'pipeline', {
FilesetSelector('ute_echo1', 'ute_echo1', dicom_format),
FilesetSelector('ute_echo2', 'ute_echo2', dicom_format),
FilesetSelector('umap_ute', 'umap_ute', dicom_format)
})
study.conversion_to_dicom_pipeline().run(work_dir=self.work_dir)
self.assertFilesetCreated('sute_cont_dicoms', study.name)
self.assertFilesetCreated('sute_fix_dicoms', study.name)
def make_study(self):
return self.create_study(ExampleStudy,
'dummy',
inputs=[
FilesetSelector('one', text_format,
'one_input'),
FilesetSelector('ten', text_format,
'ten_input')
],
parameters={'pipeline_parameter': True})
def test_freesurfer_pipeline(self):
study = self.create_study(T1T2Study,
'freesurfer',
inputs=[
FilesetSelector('t1', 'mprage',
nifti_gz_format),
FilesetSelector('t2', 'flair',
nifti_gz_format)
])
study.freesurfer_pipeline().run(work_dir=self.work_dir)
self.assertFilesetCreated('fs_recon_all.fs.zip')
def test_t2_registration_pipeline(self):
study = self.create_study(T1T2Study,
't2_registration',
inputs=[
FilesetSelector('t1', 'mprage',
nifti_gz_format),
FilesetSelector('t2', 'flair',
nifti_gz_format)
])
study.t2_registration_pipeline().run(work_dir=self.work_dir)
self.assertFilesetCreated('t2_coreg.nii.gz', study.name)
def test_format_conversions(self):
study = self.create_study(ConversionStudy, 'conversion', [
FilesetSelector('text', text_format, 'text'),
FilesetSelector('directory', directory_format, 'directory'),
FilesetSelector('zip', zip_format, 'zip')
])
self.assertCreated(list(study.data('text_from_text'))[0])
self.assertCreated(list(study.data('directory_from_zip_on_input'))[0])
self.assertCreated(list(study.data('zip_from_directory_on_input'))[0])
self.assertCreated(list(study.data('directory_from_zip_on_output'))[0])
self.assertCreated(list(study.data('zip_from_directory_on_output'))[0])
def test_coreg_and_brain_mask(self):
study = self.create_study(TestCoregStudy,
'coreg_and_mask_study',
inputs=[
FilesetSelector('ref_primary', 'mprage',
nifti_gz_format),
FilesetSelector('tocoreg_primary',
nifti_gz_format, 'flair')
])
coreg_brain = list(study.data('tocoreg_coreg_brain'))[0]
self.assertFilesetsEqual(coreg_brain, self.reference('coreg_brain'))
def test_suvr(self):
study = self.create_study(
StaticPETStudy,
'suvr',
inputs=[
FilesetSelector('registered_volume', 'suvr_registered_volume',
nifti_gz_format),
FilesetSelector('base_mask', 'cerebellum_mask',
nifti_gz_format)
])
study.suvr_pipeline().run(work_dir=self.work_dir, plugin='Linear')
self.assertFilesetCreated('SUVR_image.nii.gz', study.name)
def test_registration(self):
study = self.create_study(CoregisteredStudy,
'registration',
inputs=[
FilesetSelector('to_register', 'flair',
nifti_gz_format),
FilesetSelector('reference', 'mprage',
nifti_gz_format)
])
pipeline = study.linear_registration_pipeline()
pipeline.run(work_dir=self.work_dir)
self.assertFilesetCreated('registered.nii.gz', study.name)
self.assertFilesetCreated('matrix.mat', study.name)
def test_concatenate(self):
study = self.create_study(NODDIStudy,
'concatenate',
inputs=[
FilesetSelector('low_b_dw_scan',
'r_l_dwi_b700_30',
mrtrix_format),
FilesetSelector('high_b_dw_scan',
'r_l_dwi_b2000_60',
mrtrix_format)
])
study.concatenate_pipeline().run(work_dir=self.work_dir)
self.assertFilesetCreated('dwi_scan.mif', study.name)
def test_order_match(self):
study = self.create_study(
TestMatchStudy, 'test_dicom',
inputs=[
FilesetSelector('gre_phase', dicom_format,
pattern=self.GRE_PATTERN, order=1,
is_regex=True),
FilesetSelector('gre_mag', dicom_format,
pattern=self.GRE_PATTERN, order=0,
is_regex=True)])
phase = list(study.data('gre_phase'))[0]
mag = list(study.data('gre_mag'))[0]
self.assertEqual(phase.name, 'gre_field_mapping_3mm_phase')
self.assertEqual(mag.name, 'gre_field_mapping_3mm_mag')
def test_id_match(self):
study = test_fileset.TestMatchStudy(
name='test_dicom',
repository=XnatRepository(project_id=self.project,
server=SERVER,
cache_dir=tempfile.mkdtemp()),
processor=LinearProcessor(self.work_dir),
inputs=[
FilesetSelector('gre_phase', dicom_format, id=7),
FilesetSelector('gre_mag', dicom_format, id=6)
])
phase = list(study.data('gre_phase'))[0]
mag = list(study.data('gre_mag'))[0]
self.assertEqual(phase.name, 'gre_field_mapping_3mm_phase')
self.assertEqual(mag.name, 'gre_field_mapping_3mm_mag')
def test_per_session_prereqs(self):
study = self.create_study(
ExistingPrereqStudy,
self.STUDY_NAME,
inputs=[FilesetSelector('one', text_format, 'one')])
study.data('thousand')
targets = {
'subject1': {
'visit1': 1100.0,
'visit2': 1110.0,
'visit3': 1000.0
},
'subject2': {
'visit1': 1111.0,
'visit2': 1110.0,
'visit3': 1000.0
}
}
tree = self.repository.tree()
for subj_id, visits in self.PROJECT_STRUCTURE.items():
for visit_id in visits:
session = tree.subject(subj_id).session(visit_id)
fileset = session.fileset('thousand', study=self.STUDY_NAME)
self.assertContentsEqual(fileset, targets[subj_id][visit_id],
"{}:{}".format(subj_id, visit_id))
if subj_id == 'subject1' and visit_id == 'visit3':
self.assertNotIn(
'ten', [d.name for d in session.filesets],
"'ten' should not be generated for "
"subject1:visit3 as hundred and thousand are "
"already present")
def test_fields_roundtrip(self):
repository = XnatRepository(server=SERVER,
cache_dir=self.cache_dir,
project_id=self.project)
study = DummyStudy(
self.STUDY_NAME,
repository,
processor=LinearProcessor('a_dir'),
inputs=[FilesetSelector('source1', text_format, 'source1')])
fields = ['field{}'.format(i) for i in range(1, 4)]
sink = study.sink(outputs=fields, name='fields_sink')
sink.inputs.field1_field = field1 = 1
sink.inputs.field2_field = field2 = 2.0
sink.inputs.field3_field = field3 = str('3')
sink.inputs.subject_id = self.SUBJECT
sink.inputs.visit_id = self.VISIT
sink.inputs.desc = "Test sink of fields"
sink.inputs.name = 'test_sink'
sink.run()
source = study.source(inputs=fields, name='fields_source')
source.inputs.visit_id = self.VISIT
source.inputs.subject_id = self.SUBJECT
source.inputs.desc = "Test source of fields"
source.inputs.name = 'test_source'
results = source.run()
self.assertEqual(results.outputs.field1_field, field1)
self.assertEqual(results.outputs.field2_field, field2)
self.assertEqual(results.outputs.field3_field, field3)
class TestDicomTagMatch(BaseTestCase):
IMAGE_TYPE_TAG = ('0008', '0008')
GRE_PATTERN = 'gre_field_mapping_3mm.*'
PHASE_IMAGE_TYPE = ['ORIGINAL', 'PRIMARY', 'P', 'ND']
MAG_IMAGE_TYPE = ['ORIGINAL', 'PRIMARY', 'M', 'ND', 'NORM']
DICOM_MATCH = [
FilesetSelector('gre_phase',
dicom_format,
GRE_PATTERN,
dicom_tags={IMAGE_TYPE_TAG: PHASE_IMAGE_TYPE},
is_regex=True),
FilesetSelector('gre_mag',
dicom_format,
GRE_PATTERN,
dicom_tags={IMAGE_TYPE_TAG: MAG_IMAGE_TYPE},
is_regex=True)
]
def test_dicom_match(self):
study = self.create_study(TestMatchStudy,
'test_dicom',
inputs=self.DICOM_MATCH)
phase = list(study.data('gre_phase'))[0]
mag = list(study.data('gre_mag'))[0]
self.assertEqual(phase.name, 'gre_field_mapping_3mm_phase')
self.assertEqual(mag.name, 'gre_field_mapping_3mm_mag')
def test_order_match(self):
study = self.create_study(TestMatchStudy,
'test_dicom',
inputs=[
FilesetSelector('gre_phase',
dicom_format,
pattern=self.GRE_PATTERN,
order=1,
is_regex=True),
FilesetSelector('gre_mag',
dicom_format,
pattern=self.GRE_PATTERN,
order=0,
is_regex=True)
])
phase = list(study.data('gre_phase'))[0]
mag = list(study.data('gre_mag'))[0]
self.assertEqual(phase.name, 'gre_field_mapping_3mm_phase')
self.assertEqual(mag.name, 'gre_field_mapping_3mm_mag')
def test_repository_roundtrip(self):
study = DummyStudy(self.STUDY_NAME,
self.repository,
processor=LinearProcessor('a_dir'),
inputs=[
FilesetSelector('source1', text_format,
'source1'),
FilesetSelector('source2', text_format,
'source2'),
FilesetSelector('source3', text_format,
'source3'),
FilesetSelector('source4', text_format,
'source4')
])
# TODO: Should test out other file formats as well.
source_files = ('source1', 'source2', 'source3', 'source4')
sink_files = ('sink1', 'sink3', 'sink4')
inputnode = pe.Node(IdentityInterface(['subject_id', 'visit_id']),
'inputnode')
inputnode.inputs.subject_id = self.SUBJECT
inputnode.inputs.visit_id = self.VISIT
source = study.source(source_files)
sink = study.sink(sink_files)
sink.inputs.name = 'repository_sink'
sink.inputs.desc = (
"A test session created by repository roundtrip unittest")
# Create workflow connecting them together
workflow = pe.Workflow('source_sink_unit_test', base_dir=self.work_dir)
workflow.add_nodes((source, sink))
workflow.connect(inputnode, 'subject_id', source, 'subject_id')
workflow.connect(inputnode, 'visit_id', source, 'visit_id')
workflow.connect(inputnode, 'subject_id', sink, 'subject_id')
workflow.connect(inputnode, 'visit_id', sink, 'visit_id')
for source_name in source_files:
if not source_name.endswith('2'):
sink_name = source_name.replace('source', 'sink')
workflow.connect(source, source_name + PATH_SUFFIX, sink,
sink_name + PATH_SUFFIX)
workflow.run()
# Check local directory was created properly
outputs = [
f for f in sorted(
os.listdir(self.get_session_dir(from_study=self.STUDY_NAME)))
if not (f == DirectoryRepository.FIELDS_FNAME)
]
self.assertEqual(outputs,
['.derived', 'sink1.txt', 'sink3.txt', 'sink4.txt'])
def test_brain_extraction_pipelines(self):
study = self.create_study(T1T2Study,
'brain_mask',
inputs=[
FilesetSelector('t1', 'mprage',
nifti_gz_format),
FilesetSelector('t2', 'flair',
nifti_gz_format),
FilesetSelector('manual_wmh_mask',
nifti_gz_format,
'manual_wmh_mask')
])
study.t1_brain_extraction_pipeline().run(work_dir=self.work_dir)
study.manual_wmh_mask_registration_pipeline().run(
work_dir=self.work_dir)
for fname in ('t1_brain.nii.gz', 't2_brain.nii.gz',
'brain_mask.nii.gz', 'manual_wmh_mask_coreg.nii.gz'):
self.assertFilesetCreated(fname, study.name)
def test_module_load_in_map(self):
study = self.create_study(
RequirementsStudy, 'requirements',
[FilesetSelector('ones', text_format, 'ones')])
threes = study.data('threes')
fours = study.data('fours')
self.assertEqual(next(iter(threes)).value, 3)
self.assertEqual(next(iter(fours)).value, 4)
self.assertEqual(ModulesEnvironment.preloaded(), {})
def test_multi_multi_study(self):
study = self.create_study(
MultiMultiStudy,
'multi_multi', [
FilesetSelector('ss1_x', text_format, 'ones'),
FilesetSelector('ss1_y', text_format, 'ones'),
FilesetSelector('full_a', text_format, 'ones'),
FilesetSelector('full_b', text_format, 'ones'),
FilesetSelector('full_c', text_format, 'ones'),
FilesetSelector('partial_a', text_format, 'ones'),
FilesetSelector('partial_b', text_format, 'ones'),
FilesetSelector('partial_c', text_format, 'ones')
],
parameters=[
Parameter('full_required_op', 'mul'),
Parameter('partial_ss2_product_op', 'mul')
])
self.assertContentsEqual(study.data('g'), 11.0)
# Test parameter values in MultiStudy
self.assertEqual(study._get_parameter('full_p1').value, 100)
self.assertEqual(study._get_parameter('full_p2').value, '200')
self.assertEqual(study._get_parameter('full_p3').value, 300.0)
self.assertEqual(study._get_parameter('full_q1').value, 150)
self.assertEqual(study._get_parameter('full_q2').value, '250')
self.assertEqual(study._get_parameter('full_required_op').value, 'mul')
# Test parameter values in SubStudy
ss1 = study.sub_study('full').sub_study('ss1')
self.assertEqual(ss1._get_parameter('o1').value, 100)
self.assertEqual(ss1._get_parameter('o2').value, '200')
self.assertEqual(ss1._get_parameter('o3').value, 300.0)
ss2 = study.sub_study('full').sub_study('ss2')
self.assertEqual(ss2._get_parameter('o1').value, 150)
self.assertEqual(ss2._get_parameter('o2').value, '250')
self.assertEqual(ss2._get_parameter('o3').value, 300.0)
self.assertEqual(ss2._get_parameter('product_op').value, 'mul')
# Test parameter values in MultiStudy
self.assertEqual(study._get_parameter('partial_p1').value, 1000)
self.assertEqual(study._get_parameter('partial_ss1_o2').value, '2')
self.assertEqual(study._get_parameter('partial_ss1_o3').value, 3.0)
self.assertEqual(study._get_parameter('partial_ss2_o2').value, '20')
self.assertEqual(study._get_parameter('partial_ss2_o3').value, 30.0)
self.assertEqual(
study._get_parameter('partial_ss2_product_op').value, 'mul')
# Test parameter values in SubStudy
ss1 = study.sub_study('partial').sub_study('ss1')
self.assertEqual(ss1._get_parameter('o1').value, 1000)
self.assertEqual(ss1._get_parameter('o2').value, '2')
self.assertEqual(ss1._get_parameter('o3').value, 3.0)
ss2 = study.sub_study('partial').sub_study('ss2')
self.assertEqual(ss2._get_parameter('o1').value, 1000)
self.assertEqual(ss2._get_parameter('o2').value, '20')
self.assertEqual(ss2._get_parameter('o3').value, 30.0)
self.assertEqual(ss2._get_parameter('product_op').value, 'mul')
def test_reg(self):
study = self.create_study(DynamicPETStudy,
'reg',
inputs=[
FilesetSelector('pet_volumes',
'pet_image',
nifti_gz_format)
])
study.ICA_pipeline().run(work_dir=self.work_dir, plugin='Linear')
self.assertFilesetCreated('decomposed_file.nii.gz', study.name)
