def test_to3d():
input_map = dict(
args=dict(argstr='%s', ),
assumemosaic=dict(argstr='-assume_dicom_mosaic', ),
datatype=dict(argstr='-datum %s', ),
environ=dict(usedefault=True, ),
filetype=dict(argstr='-%s', ),
funcparams=dict(argstr='-time:zt %s alt+z2', ),
ignore_exception=dict(usedefault=True, ),
infolder=dict(
argstr='%s/*.dcm',
mandatory=True,
),
out_file=dict(
argstr='-prefix %s',
mandatory=True,
),
outputtype=dict(),
skipoutliers=dict(argstr='-skip_outliers', ),
)
instance = afni.To3D()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(instance.inputs.traits()[key],
metakey), value
def genrDcm2afni(hq, dcm, oniiDir, series_desc, series_id, quality, scanType):
if not os.path.exists(oniiDir):
os.makedirs(oniiDir)
#set the output name based on the seq_dictionary
if hq:
afniOutName = seq_dict[series_desc] + '_' + str(series_id) + '_best'
else:
afniOutName = seq_dict[series_desc] + '_' + str(series_id)
if os.path.exists(os.path.join(oniiDir, afniOutName)) and not overwriteExisting:
return
#gather some fun facts about the scan
dcmInfo = pd.read_file(dcm, stop_before_pixels=True)
repetitionTime = int(dcmInfo.RepetitionTime)
numRepetitions = int(dcmInfo.NumberOfTemporalPositions)
numImages = int(dcmInfo.ImagesInAcquisition)
#this tag is not easy to get, so you need to first get a pydicom friendly tag
nSlices = int(dcmInfo[pd.tag.BaseTag('2166863L')].value)
if not nSlices == (numImages/numRepetitions):
print('something is not right...num slices not matching num dicom images')
#run the to3d conversion
optStr = '-session ' + oniiDir + ' -time:zt ' + str(nSlices) + ' ' + str(numRepetitions) + ' ' + str(repetitionTime)
to3d = afni.To3D(in_folder=os.path.dirname(dcm), out_file=afniOutName, outputtype='AFNI', args=optStr, terminal_output='stream', ignore_exception=True)
print(to3d.cmdline)
to3d.run()
addcmd = True
if convertIdc:
#remove any instance of R-number...
addcmd = False
logFile = os.path.join(oniiDir, afniOutName + '.log')
write_log(to3d.cmdline, logFile, addcmd, [series_id, series_desc, quality, scanType])
def t1_pipeline(name='t1_preproc'):
inputnode = pe.Node(utility.IdentityInterface(fields=['t1_dicom_dir']),
name='inputspec')
outputnode = pe.Node(utility.IdentityInterface(
fields=['mask', 'corrected_t1', 'corrected_t1_brain']),
name='outputspec')
n_t1_dicom_files = pe.Node(nio.DataGrabber(sort_filelist=True, ),
name='t1_dicom_files')
n_to3d_t1 = pe.Node(afni.To3D(filetype='anat',
environ=dict(AFNI_DICOM_RESCALE='YES')),
name='to3d_t1')
n_reorient_t1 = pe.Node(afni.Resample(orientation='RPI'),
name='reorient_t1')
n_autobox_t1 = pe.Node(afni.Autobox(padding=5), name='autobox_t1')
n_zcut_t1 = pe.Node(afni.ZCutUp(outputtype='NIFTI'), name='zcut_t1')
n_newsegment_t1 = pe.Node(spm.NewSegment(
write_deformation_fields=[True, True],
channel_info=(0.0001, 60, (True, True))),
name='newsegment_t1')
n_seg2mask = pe.Node(fsl.MultiImageMaths(
output_type='NIFTI',
op_string=' -add %s -add %s -thr 0.8 -bin -eroF -dilF -dilF'),
name='seg2mask')
n_mask_brain = pe.Node(interface=fsl.ImageMaths(op_string='-mul',
suffix='_brain',
output_type='NIFTI'),
name='mask_brain')
w = pe.Workflow(name=name)
def zmax2keep(z):
return '%d %d' % (max(0, z - 174), z)
w.connect([
(inputnode, n_t1_dicom_files, [('t1_dicom_dir', 'base_directory')]),
(n_t1_dicom_files, n_to3d_t1,
[(('outfiles', sort_t1_files), 'in_files'),
(('outfiles', t1_filename, 'nii.gz'), 'out_file')]),
(n_to3d_t1, n_reorient_t1, [('out_file', 'in_file')]),
(n_reorient_t1, n_autobox_t1, [('out_file', 'in_file')]),
(n_reorient_t1, n_zcut_t1, [('out_file', 'in_file')]),
(n_autobox_t1, n_zcut_t1, [(('z_max', zmax2keep), 'keep')]),
(n_zcut_t1, n_newsegment_t1, [('out_file', 'channel_files')]),
(n_newsegment_t1, n_seg2mask, [(('native_class_images', getitem_rec, 0,
0), 'in_file'),
(('native_class_images', getitem_rec,
slice(1, 3), 0), 'operand_files')]),
(n_zcut_t1, n_seg2mask, [(('out_file', fname_presuffix_basename, '',
'_mask', '.'), 'out_file')]),
(n_newsegment_t1, n_mask_brain, [('bias_corrected_images', 'in_file')
]),
(n_seg2mask, n_mask_brain, [('out_file', 'in_file2')]),
(n_seg2mask, outputnode, [('out_file', 'mask')]),
(n_newsegment_t1, outputnode, [('bias_corrected_images',
'corrected_t1')])
])
return w