def test_freesurfer_pipeline(self):
study = self.create_study(
T1T2Study, 'freesurfer', inputs=[
DatasetMatch('t1', nifti_gz_format, 'mprage'),
DatasetMatch('t2', nifti_gz_format, 'flair')])
study.freesurfer_pipeline().run(work_dir=self.work_dir)
self.assertDatasetCreated('fs_recon_all.fs.zip')
def test_preprocess(self):
study = self.create_study(DiffusionStudy, 'preprocess', [
DatasetMatch('primary', mrtrix_format, 'r_l_dwi_b700_30'),
DatasetMatch('dwi_reference', mrtrix_format, 'l_r_dwi_b0_6')
])
preproc = study.data('preproc')[0]
self.assertTrue(os.path.exists(preproc.path))
def test_t2_registration_pipeline(self):
study = self.create_study(
T1T2Study, 't2_registration', inputs=[
DatasetMatch('t1', nifti_gz_format, 'mprage'),
DatasetMatch('t2', nifti_gz_format, 'flair')])
study.t2_registration_pipeline().run(work_dir=self.work_dir)
self.assertDatasetCreated('t2_coreg.nii.gz', study.name)
def test_registration_to_matrix(self):
study = self.create_study(
CoregisteredToMatrixStudy, 'registration_to_matrix', {
DatasetMatch('to_register', nifti_gz_format, 'flair'),
DatasetMatch('reference', nifti_gz_format, 'mprage'),
DatasetMatch('matrix', text_matrix_format, 'matrix')})
study.linear_registration_pipeline().run(work_dir=self.work_dir)
self.assertDatasetCreated('registered.nii.gz', study.name)
def test_extract_b0(self):
study = self.create_study(DiffusionStudy, 'extract_b0', [
DatasetMatch('preproc', nifti_gz_format, 'preproc'),
DatasetMatch('grad_dirs', fsl_bvecs_format, 'gradient_dirs'),
DatasetMatch('bvalues', fsl_bvals_format, 'bvalues')
])
study.extract_b0_pipeline().run(work_dir=self.work_dir)
self.assertDatasetCreated('primary.nii.gz', study.name)
def test_bias_correct(self):
study = self.create_study(DiffusionStudy, 'bias_correct', [
DatasetMatch('preproc', nifti_gz_format, 'preproc'),
DatasetMatch('grad_dirs', fsl_bvecs_format, 'gradient_dirs'),
DatasetMatch('bvalues', fsl_bvals_format, 'bvalues')
])
study.bias_correct_pipeline(mask_tool='mrtrix').run(
work_dir=self.work_dir)
self.assertDatasetCreated('bias_correct.nii.gz', study.name)
def test_tensor(self):
study = self.create_study(DiffusionStudy, 'tensor', [
DatasetMatch('bias_correct', nifti_gz_format, 'bias_correct'),
DatasetMatch('brain_mask', nifti_gz_format, 'brain_mask'),
DatasetMatch('grad_dirs', fsl_bvecs_format, 'gradient_dirs'),
DatasetMatch('bvalues', fsl_bvals_format, 'bvalues')
])
study.tensor_pipeline().run(work_dir=self.work_dir)
self.assertDatasetCreated('tensor.nii.gz', study.name)
def test_response(self):
study = self.create_study(DiffusionStudy, 'response', [
DatasetMatch('bias_correct', nifti_gz_format, 'bias_correct'),
DatasetMatch('brain_mask', nifti_gz_format, 'brain_mask'),
DatasetMatch('grad_dirs', fsl_bvecs_format, 'gradient_dirs'),
DatasetMatch('bvalues', fsl_bvals_format, 'bvalues')
])
study.response_pipeline().run(work_dir=self.work_dir)
self.assertDatasetCreated('response.txt', study.name)
def test_ute(self):
study = self.create_study(
UTEStudy, 'pipeline', {
DatasetMatch('ute_echo1', dicom_format, 'ute_echo1'),
DatasetMatch('ute_echo2', dicom_format, 'ute_echo2'),
DatasetMatch('umap_ute', dicom_format, 'umap_ute')
})
study.conversion_to_dicom_pipeline().run(work_dir=self.work_dir)
self.assertDatasetCreated('sute_cont_dicoms', study.name)
self.assertDatasetCreated('sute_fix_dicoms', study.name)
def test_registration(self):
study = self.create_study(
CoregisteredStudy, 'registration',
inputs=[
DatasetMatch('to_register', nifti_gz_format, 'flair'),
DatasetMatch('reference', nifti_gz_format, 'mprage')])
pipeline = study.linear_registration_pipeline()
pipeline.run(work_dir=self.work_dir)
self.assertDatasetCreated('registered.nii.gz', study.name)
self.assertDatasetCreated('matrix.mat', study.name)
def test_coreg_and_brain_mask(self):
study = self.create_study(TestCoregStudy,
'coreg_and_mask_study',
inputs=[
DatasetMatch('ref_primary',
nifti_gz_format, 'mprage'),
DatasetMatch('tocoreg_primary',
nifti_gz_format, 'flair')
])
coreg_brain = study.data('tocoreg_coreg_brain')[0]
self.assertDatasetsEqual(coreg_brain, self.reference('coreg_brain'))
def test_brain_extraction_pipelines(self):
study = self.create_study(
T1T2Study, 'brain_mask', inputs=[
DatasetMatch('t1', nifti_gz_format, 'mprage'),
DatasetMatch('t2', nifti_gz_format, 'flair'),
DatasetMatch('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.assertDatasetCreated(fname, study.name)
def test_concatenate(self):
study = self.create_study(NODDIStudy,
'concatenate',
inputs=[
DatasetMatch('low_b_dw_scan',
mrtrix_format,
'r_l_dwi_b700_30'),
DatasetMatch('high_b_dw_scan',
mrtrix_format,
'r_l_dwi_b2000_60')
])
study.concatenate_pipeline().run(work_dir=self.work_dir)
self.assertDatasetCreated('dwi_scan.mif', study.name)
def test_suvr(self):
study = self.create_study(StaticPETStudy,
'suvr',
inputs=[
DatasetMatch('registered_volume',
nifti_gz_format,
'suvr_registered_volume'),
DatasetMatch('base_mask',
nifti_gz_format,
'cerebellum_mask')
])
study.suvr_pipeline().run(work_dir=self.work_dir, plugin='Linear')
self.assertDatasetCreated('SUVR_image.nii.gz', study.name)
def test_reg(self):
study = self.create_study(
DynamicPETStudy,
'reg',
inputs=[DatasetMatch('pet_volumes', nifti_gz_format, 'pet_image')])
study.ICA_pipeline().run(work_dir=self.work_dir, plugin='Linear')
self.assertDatasetCreated('decomposed_file.nii.gz', study.name)
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 = [
DatasetMatch('gre_phase',
dicom_format,
GRE_PATTERN,
dicom_tags={IMAGE_TYPE_TAG: PHASE_IMAGE_TYPE},
is_regex=True),
DatasetMatch('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 = study.data('gre_phase')[0]
mag = 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=[
DatasetMatch('gre_phase',
dicom_format,
pattern=self.GRE_PATTERN,
order=1,
is_regex=True),
DatasetMatch('gre_mag',
dicom_format,
pattern=self.GRE_PATTERN,
order=0,
is_regex=True)
])
phase = study.data('gre_phase')[0]
mag = 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_pipeline_prerequisites(self):
study = self.create_study(ConversionStudy, 'conversion', [
DatasetMatch('mrtrix', text_format, 'mrtrix'),
DatasetMatch('nifti_gz', text_format, 'nifti_gz'),
DatasetMatch('dicom', dicom_format, 't1_mprage_sag_p2_iso_1_ADNI'),
DatasetMatch('directory', directory_format,
't1_mprage_sag_p2_iso_1_ADNI'),
DatasetMatch('zip', zip_format, 'zip')
])
study.data('nifti_gz_from_dicom')
study.data('mrtrix_from_nifti_gz')
study.data('nifti_from_mrtrix')
study.data('directory_from_zip')
study.data('zip_from_directory')
self.assertDatasetCreated('nifti_gz_from_dicom.nii.gz', study.name)
self.assertDatasetCreated('mrtrix_from_nifti_gz.mif', study.name)
self.assertDatasetCreated('nifti_from_mrtrix.nii', study.name)
self.assertDatasetCreated('directory_from_zip', study.name)
self.assertDatasetCreated('zip_from_directory.zip', study.name)
def test_order_match(self):
study = self.create_study(TestMatchStudy,
'test_dicom',
inputs=[
DatasetMatch('gre_phase',
dicom_format,
pattern=self.GRE_PATTERN,
order=1,
is_regex=True),
DatasetMatch('gre_mag',
dicom_format,
pattern=self.GRE_PATTERN,
order=0,
is_regex=True)
])
phase = study.data('gre_phase')[0]
mag = 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_dcm2niix(self):
study = self.create_study(DummyStudy,
'concatenate',
inputs=[
DatasetMatch('input_dataset',
dicom_format,
't2_tse_tra_p2_448')
])
study.data('output_dataset')[0]
self.assertDatasetCreated('output_dataset.nii.gz', study.name)
def test_qsm_de_pipeline(self):
study = self.create_study(
T2StarStudy,
'qsm',
inputs=[DatasetMatch('coils', zip_format, 'swi_coils')])
study.qsm_pipeline().run(work_dir=self.work_dir)
for fname in ('qsm.nii.gz', 'tissue_phase.nii.gz',
'tissue_mask.nii.gz', 'qsm_mask.nii.gz'):
self.assertDatasetCreated(dataset_name=fname,
study_name=study.name)
def test_average_response(self):
study = self.create_study(
DiffusionStudy, 'response',
{DatasetMatch('response', text_format, 'response')})
study.average_response_pipeline().run(work_dir=self.work_dir)
for subject_id in self.subject_ids:
for visit_id in self.visit_ids(subject_id):
self.assertDatasetCreated('avg_response.txt',
study.name,
subject=subject_id,
visit=visit_id)
def test_intensity_normalization(self):
study = self.create_study(DiffusionStudy, 'intens_norm', [
DatasetMatch('bias_correct', nifti_gz_format, 'biascorrect'),
DatasetMatch('brain_mask', nifti_gz_format, 'brainmask'),
DatasetMatch('grad_dirs', fsl_bvecs_format, 'gradientdirs'),
DatasetMatch('bvalues', fsl_bvals_format, 'bvalues')
])
study.intensity_normalisation_pipeline().run(work_dir=self.work_dir)
for subject_id in self.subject_ids:
for visit_id in self.visit_ids(subject_id):
self.assertDatasetCreated('norm_intensity.mif',
study.name,
subject=subject_id,
visit=visit_id)
self.assertDatasetCreated('norm_intens_fa_template.mif',
study.name,
frequency='per_project')
self.assertDatasetCreated('norm_intens_wm_mask.mif',
study.name,
frequency='per_project')
WORK_PATH = os.path.join('/scratch', 'dq13', 'aspree', 'qsm')
CACHE_PROJECT_PATH = os.path.join(WORK_PATH, 'project.pkl')
try:
os.makedirs(WORK_PATH)
except OSError as e:
if e.errno != errno.EEXIST:
raise
session_ids_path = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'..', 'resources',
'old_swi_coils_remaining.txt')
print(session_ids_path)
with open(session_ids_path) as f:
ids = f.read().split()
PROJECT_ID = 'MRH017'
datasets = {DatasetMatch('coils', zip_format, 'swi_coils')}
visit_ids = visit_ids['MR01']
repository = XnatRepository(cache_dir='/scratch/dq13/xnat_cache3')
if args.cache_project:
project = repository.project(PROJECT_ID,
subject_ids=ids,
visit_ids=visit_ids)
with open(CACHE_PROJECT_PATH, 'w') as f:
pkl.dump(project, f)
else:
with open(CACHE_PROJECT_PATH) as f:
project = pkl.load(f)
repository.cache(PROJECT_ID,
repository_path = os.path.abspath(
os.path.join(os.environ['HOME'], 'Data', 'MBI', 'noddi'))
BASE_WORK_PATH = os.path.abspath(
os.path.join(os.environ['HOME'], 'Data', 'MBI', 'work'))
NODDI_PROJECT = 'pilot'
NODDI_SUBJECT = 'SUBJECT1'
NODDI_SESSION = 'SESSION1'
WORK_PATH = os.path.abspath(os.path.join(BASE_WORK_PATH, 'noddi'))
SESSION_DIR = os.path.join(repository_path, NODDI_PROJECT, NODDI_SUBJECT,
NODDI_SESSION)
DATASET_NAME = 'noddi'
shutil.rmtree(WORK_PATH, ignore_errors=True)
os.makedirs(WORK_PATH)
study = NODDIStudy(name=DATASET_NAME,
project_id=NODDI_PROJECT,
repository=LocalRepository(repository_path),
input_scans=[
DatasetMatch('low_b_dw_scan', mrtrix_format,
'r_l_noddi_b700_30_directions'),
DatasetMatch('high_b_dw_scan', mrtrix_format,
'r_l_noddi_b2000_60_directions'),
DatasetMatch('forward_rpe', mrtrix_format,
'r_l_noddi_b0_6'),
DatasetMatch('reverse_rpe', mrtrix_format,
'l_r_noddi_b0_6')
])
study.noddi_fitting_pipeline().run(work_dir=WORK_PATH)
def create_fmri_study_class(name,
t1,
epis,
epi_number,
echo_spacing,
fm_mag=None,
fm_phase=None,
run_regression=False):
inputs = []
dct = {}
data_specs = []
parameter_specs = []
output_files = []
distortion_correction = False
if fm_mag and fm_phase:
logger.info('Both magnitude and phase field map images provided. EPI '
'ditortion correction will be performed.')
distortion_correction = True
elif fm_mag or fm_phase:
logger.info(
'In order to perform EPI ditortion correction both magnitude '
'and phase field map images must be provided.')
else:
logger.info(
'No field map image provided. Distortion correction will not be'
'performed.')
study_specs = [SubStudySpec('t1', T1Study)]
ref_spec = {'t1_brain': 'coreg_ref_brain'}
inputs.append(
DatasetMatch('t1_primary', dicom_format, t1, is_regex=True, order=0))
epi_refspec = ref_spec.copy()
epi_refspec.update({
't1_wm_seg': 'coreg_ref_wmseg',
't1_preproc': 'coreg_ref_preproc',
'train_data': 'train_data'
})
study_specs.append(SubStudySpec('epi_0', FunctionalMRIStudy, epi_refspec))
if epi_number > 1:
epi_refspec.update({
't1_wm_seg': 'coreg_ref_wmseg',
't1_preproc': 'coreg_ref_preproc',
'train_data': 'train_data',
'epi_0_coreg_to_atlas_warp': 'coreg_to_atlas_warp',
'epi_0_coreg_to_atlas_mat': 'coreg_to_atlas_mat'
})
study_specs.extend(
SubStudySpec('epi_{}'.format(i), FunctionalMRIStudy, epi_refspec)
for i in range(1, epi_number))
for i in range(epi_number):
inputs.append(
DatasetMatch('epi_{}_primary'.format(i),
dicom_format,
epis,
order=i,
is_regex=True))
parameter_specs.append(
ParameterSpec('epi_{}_fugue_echo_spacing'.format(i), echo_spacing))
if distortion_correction:
inputs.extend(
DatasetMatch('epi_{}_field_map_mag'.format(i),
dicom_format,
fm_mag,
dicom_tags={IMAGE_TYPE_TAG: MAG_IMAGE_TYPE},
is_regex=True,
order=0) for i in range(epi_number))
inputs.extend(
DatasetMatch('epi_{}_field_map_phase'.format(i),
dicom_format,
fm_phase,
dicom_tags={IMAGE_TYPE_TAG: PHASE_IMAGE_TYPE},
is_regex=True,
order=0) for i in range(epi_number))
if run_regression:
output_files.extend('epi_{}_smoothed_ts'.format(i)
for i in range(epi_number))
else:
output_files.extend('epi_{}_fix_dir'.format(i)
for i in range(epi_number))
dct['add_sub_study_specs'] = study_specs
dct['add_data_specs'] = data_specs
dct['add_parameter_specs'] = parameter_specs
dct['__metaclass__'] = MultiStudyMetaClass
return (MultiStudyMetaClass(name, (FunctionalMRIMixin, ),
dct), inputs, output_files)
