def methods_02_lpa( input_dir, verbose ):
cenc_dirs = cenc.directories( input_dir)
wm_lesions_dir = cenc_dirs['wmlesions']['dirs']['root']
wm_lesions_lpa_dir = cenc_dirs['wmlesions']['dirs']['lpa']
util.mkcd_dir( [ cenc_dirs['wmlesions']['dirs']['lpa'] ], True)
util.copy_inputs( [ os.path.join( cenc_dirs['wmlesions']['dirs']['register'], 't2flair_Affine_nu__t2flair_Warped.nii.gz') ],
cenc_dirs['wmlesions']['dirs']['lpa'] )
glob_files = glob.glob('*.gz')
for ii in glob_files:
os.system('gunzip ' + ii)
os.chmod( str.replace(ii, '.gz',''), stat.S_IRUSR | stat.S_IRGRP | stat.S_IWUSR | stat.S_IWGRP)
# Run Matlab
matlab_run_command = "ps_LST_lpa('" + os.path.join( cenc_dirs['wmlesions']['dirs']['lpa'], 't2flair_Affine_nu__t2flair_Warped.nii') + "',''); exit;"
command = [ 'matlab', '-nojvm', '-nosplash', '-nodesktop', '-r', matlab_run_command ]
util.iw_subprocess( command, verbose, verbose, False)
os.chdir(cenc_dirs['wmlesions']['dirs']['lpa'])
for ii in glob.glob('*.nii'):
os.system('gzip ' + ii)
os.chmod( str.replace(ii, '.nii','.nii.gz'), stat.S_IRUSR | stat.S_IRGRP | stat.S_IWUSR | stat.S_IWGRP)
def prepare( input_dir ):
cenc_dirs = cenc.directories( input_dir)
wm_lesions_dir = cenc_dirs['wmlesions']['dirs']['root']
wm_lesions_input_dir = cenc_dirs['wmlesions']['dirs']['input']
wm_lesions_lpa_dir = cenc_dirs['wmlesions']['dirs']['lpa']
# Create Input Directory if it doesn't exist and link files
files = [ os.path.abspath( os.path.join( cenc_dirs['results']['dirs']['images'], 'nu.nii.gz')),
os.path.abspath( os.path.join( cenc_dirs['results']['dirs']['labels'], 'mask.nii.gz')),
os.path.abspath( os.path.join( cenc_dirs['cenc']['reorient'], 't2flair.nii.gz'))
]
util.mkcd_dir( os.path.join( wm_lesions_input_dir ) )
util.link_inputs( files, wm_lesions_input_dir )
def methods_01_register( input_dir, verbose ):
cenc_dirs = cenc.directories( input_dir)
wm_lesions_dir = cenc_dirs['wmlesions']['dirs']['root']
wm_lesions_lpa_dir = cenc_dirs['wmlesions']['dirs']['lpa']
# Register
util.mkcd_dir( [ cenc_dirs['wmlesions']['dirs']['register'] ], True)
files = [ os.path.join( cenc_dirs['wmlesions']['dirs']['input'], 'nu.nii.gz'),
os.path.join( cenc_dirs['wmlesions']['dirs']['input'], 't2flair.nii.gz'),
os.path.join( cenc_dirs['results']['dirs']['labels'], 'mask.nii.gz')
]
util.link_inputs( files, cenc_dirs['wmlesions']['dirs']['register'] )
ants_register('t2flair')
def results( input_dir):
cenc_dirs = cenc.directories( input_dir)
util.mkcd_dir( [ cenc_dirs['wmlesions']['dirs']['results'] ], True)
# Link Files
link_result_files = [ [ os.path.join( cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_t2flair.nii.gz'),
os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_t2flair.nii.gz') ],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['input'], 'nu.nii.gz'),
os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'nu.nii.gz') ],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_pmap.nii.gz'),
os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_pmap.nii.gz')],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions.json'),
os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions.json')],
[os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_labels.nii.gz')],
[os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.csv'),
os.path.join(cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_labels.csv')],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_mask.nii.gz'),
os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_mask.nii.gz')],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_pmap.nii.gz'),
os.path.join( cenc_dirs['results']['dirs']['images'], 'wmlesions_lpa_pmap.nii.gz')],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_mask.nii.gz'),
os.path.join( cenc_dirs['results']['dirs']['labels'], 'wmlesions_lpa_mask.nii.gz')],
[ os.path.join( cenc_dirs['wmlesions']['dirs']['results'], 'wmlesions_lpa_labels.nii.gz'),
os.path.join( cenc_dirs['results']['dirs']['labels'], 'wmlesions_lpa_labels.nii.gz')
]
]
for ii in link_result_files:
util.force_hard_link( ii[0], ii[1])
def methods_03_stats(input_dir, lower_threshold=0.5, upper_threshold=numpy.inf, min_lesion_volume_voxels=10, verbose=False):
cenc_dirs = cenc.directories(input_dir)
wm_lesions_dir = cenc_dirs['wmlesions']['dirs']['root']
wm_lesions_lpa_dir = cenc_dirs['wmlesions']['dirs']['lpa']
util.mkcd_dir([cenc_dirs['wmlesions']['dirs']['stats']], True)
wm_lesions_stats_filename = os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.csv')
wm_lesions_pmap_stats_filename = os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_pmap.csv')
# Mask LPA Probability Mask and remove NaN
command = [['fslmaths', os.path.join(cenc_dirs['wmlesions']['dirs']['lpa'], 'ples_lpa_mt2flair_Affine_nu__t2flair_Warped.nii.gz'),
'-mas', os.path.join(cenc_dirs['wmlesions']['dirs']['input'], 'mask.nii.gz'), '-nan',
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_pmap.nii.gz')
]]
for ii in command:
util.iw_subprocess(ii, verbose, verbose, False)
# Create labels file from LPA probability map
labels.label_connected_components( os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_pmap.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
lower_threshold, upper_threshold )
# Measure properties of labels
labels.properties(os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
None, False, ['volume_mm3'],
wm_lesions_stats_filename,
limits_volume_voxels=[min_lesion_volume_voxels, numpy.inf],
limits_bb_volume_voxels=[0, numpy.inf], limits_fill_factor=[0, 1], sort='volume_mm3',
verbose=verbose, verbose_nlines=20)
# Keep labels greater than 10 mm^3. Limit is set above
labels.keep(os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
[],
wm_lesions_stats_filename,
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz')
)
# 1) Create a WM Lesions mask
# 2) Mask LPA Registered T2 FLAIR
command = [ ['fslmaths', os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
'-bin', os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_mask.nii.gz')
],
['fslmaths',
os.path.join(cenc_dirs['wmlesions']['dirs']['lpa'], 'mt2flair_Affine_nu__t2flair_Warped.nii.gz'),
'-mas', os.path.join(cenc_dirs['wmlesions']['dirs']['input'], 'mask.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_t2flair.nii.gz')
]
]
for ii in command:
util.iw_subprocess(ii, verbose, verbose, False)
# Measure Pmaps stats labels
df_pmap_measure = labels.measure( os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_labels.nii.gz'),
os.path.join(cenc_dirs['wmlesions']['dirs']['stats'], 'wmlesions_lpa_pmap.nii.gz'))
df_pmap_measure.to_csv(wm_lesions_pmap_stats_filename, index=False)
# Write out JSON file with RedCap Instrument
methods_write_json_redcap_instrument(input_dir, wm_lesions_stats_filename, wm_lesions_pmap_stats_filename, verbose)