def test_orientations():
""" Test that the axis string is correctly recovered
"""
siemens_orientations = {'3': 'Tra', '4': 'Tra', '5': 'Tra', '6': 'Sag',
'7': 'Cor', '8': 'Sag', '9': 'Cor', '10': 'Tra>Sag 30.0',
'11': 'Tra>Cor 30.0', '12': 'Tra>Cor 30.0 >Sag 15.0',
'13': 'Tra>Sag 30.0 >Cor 15.0', '14': 'Tra>Sag -30.0',
'15': 'Tra>Cor -30.0', '16': 'Tra>Cor 30.0 >Sag -15.0',
'17': 'Tra>Sag 30.0 >Cor -15.0', '18': 'Tra>Sag 30.0',
'19': 'Tra>Cor 30.0', '20': 'Tra>Cor 30.0 >Sag 15.0',
'21': 'Tra>Sag 30.0 >Cor 15.0'}
siemens_inplane_orientations = {'3': 0.0, '4': 30.0, '5': -30.0, '6': 0.0,
'7': 0.0, '8': 30.0, '9': 30.0, '10': 0.0,
'11': 0.0, '12': 0.0, '13': 0.0, '14': 0.0,
'15': 0.0, '16': 0.0, '17': 0.0, '18': 20.0,
'19': 20.0, '20': 20.0, '21': 20.0}
for idx, ori in siemens_orientations.items():
data = siemens.Siemens('tests/data/siemens/%s' % idx)
data.calculate_transform()
calc_ori = data.qform.siemens_orientation()
# orientation string is correct
assert ori == calc_ori[0]
assert abs(siemens_inplane_orientations[idx] - calc_ori[1]) < DIM_TOL
size = data.qform.get_scale()
real_size = [20, 25, 30]
position = data.qform.get_position()
for i in range(3):
assert abs(size[i]-real_size[i]) < DIM_TOL
assert abs(position[i]) < DIM_TOL
# in plane rotation
assert approx(np.degrees(data.meta['VoiInPlaneRotation'])) \
== siemens_inplane_orientations[idx]
def test_read_unaveraged():
"""Test reading a directory of dicoms
"""
data = siemens.Siemens('tests/data/siemens/eja_svs_press_combined_noave')
data.get_svsdata()
assert data.data.shape == (1, 8, 1, 1, 2048)
for i in range(data.data.shape[0]):
for j in range(data.data.shape[1]):
assert data.data[i, j, 0, 0, -1] != 0
def test_read_single():
"""Test reading a single combined DICOM
"""
test_file = 'tests/data/siemens/3/S8457LTU_2_3_00001_00001_173218510000__1263534865.dcm'
data_file = siemens.Siemens(test_file)
svs_file = data_file.get_svsdata()
data_dir = siemens.Siemens('tests/data/siemens/3')
svs_dir = data_dir.get_svsdata()
# is it the right size?
assert data_file.data.shape == data_dir.data.shape == (1, 1, 1, 1, 2048)
dcm = dcmwrapper.wrapper_from_file(test_file)
packed = dcm.get((0x7fe1, 0x1010)).value
data = struct.unpack("<%df" % (len(packed) / 4), packed)
cmplx = [data[i]+data[i+1]*1j for i in range(0, len(data), 2)]
assert (data_file.data == np.conj(cmplx)).all()
assert (data_file.data == data_dir.data).all()
def test_sidecar():
"""Test creating a BIDS sidecar
"""
# TODO: Check value consistency
data = siemens.Siemens('tests/data/siemens/eja_svs_press_uncombined')
with open('test.json', 'w+') as fp:
json.dump(data.get_sidecar(), fp, indent=3)
with open('test.json') as fp:
bids = json.load(fp)
# save the transform
np.savetxt('sub-test_from-Device_to-orig_mode-image_xfm.mat', data.qform.get_matrix())
def test_axis():
siemens_data = siemens.Siemens('tests/data/siemens/eja_svs_press_combined_noave')
data = siemens_data.get_svsdata()
sw = data.sw
npts = data.fid.shape[-1]
f = np.arange(-sw/2.0+sw/(2*npts), sw/2, sw/npts)
# is the frequnecy axis correct?
orig_f = data.f
assert approx(f) == orig_f
assert not (approx(data.f) != orig_f)
# shift the ppm axis
ppm_shifted = data.ppm - data._ppmshift
data.ppm = ppm_shifted
shifted_f = data.f
assert approx(shifted_f) != orig_f
def main():
parser = argparse.ArgumentParser(
description='Process Siemens SVS data from DICOMs')
parser.add_argument('dicom', type=str, nargs='+',
help="Path to DICOM directory")
parser.add_argument('--prefix', type=str,
help="Output prefix")
parser.add_argument('--t1', type=str, required=False,
help="Path to co-registered T1 NIFTI (saves VOI mask)")
parser.add_argument('--water', type=str, required=False,
help="Path to DICOM directory of unsupressed water data. Will be used for correction.")
parser.add_argument('--load', dest='load', action='store_true',
help="Load FID data from DICOM (required for analysis).")
parser.add_argument('--no-load', dest='load', action='store_false',
help="Do not load FID data")
parser.add_argument('--fida', dest='fida', action='store_true',
help="Run FID-A preprocessing (requires matlab)")
parser.add_argument('--no-fida', dest='fida', action='store_false',
help="Do not run FID-A processing")
parser.add_argument('--tarquin', dest='tarquin', action='store_true',
help="Run Tarquin fit")
parser.add_argument('--no-tarquin', dest='tarquin', action='store_false',
help="Do not run Tarquin fitting")
parser.add_argument('--no-concat', dest='concat', action='store_false',
help="Do not concatenate runs before preprocessing")
parser.set_defaults(load=True)
parser.set_defaults(fida=True)
parser.set_defaults(tarquin=True)
# args = parser.parse_args(['--prefix', '/Users/roh17004/Downloads/TD919/autotest3',
# '--t1','/Users/roh17004/Downloads/TD919/5/TD919-T1w.nii.gz',
# '/Users/roh17004/Downloads/TD919/39', '/Users/roh17004/Downloads/TD919/42',
# '/Users/roh17004/Downloads/TD919/45'])
args = parser.parse_args()
# Parse the DICOMs
runs = []
fida_names = []
for run,fname in enumerate(args.dicom):
print('Reading metadata from %s...\n' % fname)
dcm = siemens.Siemens(fname)
dcm.calculate_transform()
# save transform
np.savetxt('%s_run-%02d_from-device_to-orig_mode-image_xfm.mat' % (args.prefix, run+1), dcm.qform.get_matrix())
# make voxel
if args.t1 is not None:
print('Creating VOI mask...\n')
from autovps import make_voi
import nibabel as nib
tform = dcm.calculate_transform()
t1 = nib.load(args.t1)
img = make_voi.make_voi(t1, tform)
nib.save(img, '%s_run-%02d_space-orig_roi.nii.gz' % (args.prefix, run+1))
# Read data
if args.load:
print('Loading data...\n')
svs = dcm.get_svsdata()
# Save to FID-A .mat
fida_fname = '%s_run-%02d_fida.mat' % (args.prefix, run+1)
svs.save_fida(fida_fname)
fida_names.append(fida_fname)
# TODO: save NIFTI, LCM
runs.append(svs)
# merge runs
fids = runs[0].fid
specs = runs[0].spec
if len(runs) > 1:
print('Merging multiple runs...')
for idx in range(1, len(runs)):
# check the sequence is the same
if runs[0].sequence_name != runs[idx].sequence_name:
raise Exception('Not all runs are the same sequence!')
if runs[0].te != runs[idx].te:
raise Exception('Not all runs have the same TE!')
if runs[0].tr != runs[idx].tr:
raise Exception('Not all runs have the same TR!')
if runs[0].sw != runs[idx].sw:
raise Exception('Not all runs have the same spectral width!')
# TODO: add equality operator to Transform class
if not np.all(runs[0].transform.get_matrix() == runs[idx].transform.get_matrix()):
raise Exception('Not all runs are from the same location!')
# TODO: check channels match
fids = np.concatenate((fids, runs[idx].fid), 1)
specs = np.concatenate((specs, runs[idx].spec), 1)
svs = runs[0]
svs.fid = fids
svs.spec = specs
fida_fname = '%s_desc-merged_fida.mat' % (args.prefix)
svs.save_fida(fida_fname)
# Preprocess using FID-A
# TODO: Mark STEAM and sLASER
if args.fida:
water_arg = '[]'
# Load water
if args.water is not None:
dcm_w = siemens.Siemens(args.water)
svs_w = dcm_w.get_svsdata()
fida_w_fname = '%s_water.mat' % args.prefix
svs_w.save_fida(fida_w_fname)
water_arg = f"'{fida_w_fname}'"
print('Preprocessing using FID-A...')
import subprocess
from subprocess import Popen
import platform
# check for macOS and try to wake up the display
# so matlab figures will work
p = None
if platform.system() == 'Darwin':
print('macOS might not work!')
p = Popen(['caffeinate', '-u'])
if svs.sequence_type == 'PRESS':
cmd = "preproc_press('%s_report', %s, '%s'); quit" % (args.prefix, water_arg, fida_fname)
subprocess.run(['matlab', '-nodesktop', '-r', cmd])
if args.tarquin:
print('Processing PRESS spectra using Tarquin')
cmd = """tarquin --input %s_report/spectra_jmrui_ave.txt --format jmrui_txt \
--lipid_filter true --auto_phase true --auto_ref true \
--crlb_optim false --ref_signals 1h_naa_cr_cho --fs %d \
--ft %d --echo %f --pul_seq press \
--output_txt %s_report/tarquin.txt \
--output_csv %s_report/tarquin.csv \
--output_pdf %s_report/tarquin.pdf \
--output_image %s_report/tarquin_img.pdf \
--ext_pdf true --output_xml %s_report/tarquin.xml --int_basis 1h_brain_glth \
--svs_only true --stack_pdf true --te1 .014""" % (args.prefix, svs.sw, svs.larmor, svs.te, args.prefix, args.prefix, args.prefix, args.prefix, args.prefix)
print(cmd)
subprocess.run(cmd, shell=True)
if svs.sequence_type == 'MEGAPRESS':
if not args.concat:
# preprocess and then align each run separately
cmd = "preproc_multi_megapress('%s_report', %s, '%s'); quit" % (args.prefix, water_arg, "','".join(fida_names))
print('Preprocessing each run')
else:
# preprocess the merged data
cmd = "preproc_megapress('%s_report', %s, '%s'); quit" % (args.prefix, water_arg, fida_fname)
print('Preprocessing merged data')
subprocess.run(['matlab', '-nodesktop', '-r', cmd])
if args.tarquin:
print('Processing MEGA-PRESS difference spectra using Tarquin')
cmd = """tarquin --input %s_report/spectra_jmrui_diff_ave.txt --format jmrui_txt \
--lipid_filter true --auto_phase true --auto_ref true \
--crlb_optim false --ref_signals 1h_naa --fs %d \
--ft %d --echo %f --pul_seq mega_press \
--output_txt %s_report/tarquin.txt \
--output_csv %s_report/tarquin.csv \
--output_pdf %s_report/tarquin.pdf \
--output_image %s_report/tarquin_img.pdf \
--ext_pdf true --output_xml %s_report/tarquin.xml --int_basis megapress_gaba \
--svs_only true --stack_pdf true --te1 .014""" % (args.prefix, svs.sw, svs.larmor, svs.te, args.prefix, args.prefix, args.prefix, args.prefix, args.prefix)
print(cmd)
subprocess.run(cmd, shell=True)
print('Processing MEGA-PRESS off spectra using Tarquin')
cmd = """tarquin --input %s_report/spectra_jmrui_off_ave.txt --format jmrui_txt \
--lipid_filter true --auto_phase true --auto_ref true \
--crlb_optim false --ref_signals 1h_naa --fs %d \
--ft %d --echo %f --pul_seq press \
--output_txt %s_report/tarquin_off.txt \
--output_csv %s_report/tarquin_off.csv \
--output_pdf %s_report/tarquin_off.pdf \
--output_image %s_report/tarquin_off_img.pdf \
--ext_pdf true --output_xml %s_report/tarquin_off.xml --int_basis 1h_brain \
--svs_only true --stack_pdf true --te1 .014""" % (args.prefix, svs.sw, svs.larmor, svs.te, args.prefix, args.prefix, args.prefix, args.prefix, args.prefix)
print(cmd)
subprocess.run(cmd, shell=True)
if p is not None:
p.terminate()
def test_nifti():
with warns(UserWarning, match='Assuming channels are combined'):
siemens_data = siemens.Siemens(
'tests/data/siemens/eja_svs_press_combined_noave')
data = siemens_data.get_svsdata()
data.save_nifti('test.nii')
%load_ext autoreload %autoreload 2 import autovps.dataset.siemens as siemens from autovps.dataset import svsdata from autovps import make_voi import nibabel as nib base_path = '/Users/roh17004/Downloads/104DI' #press data = siemens.Siemens(base_path + '/12') svs = data.get_svsdata() svs.save_fida(base_path + '/press.mat') data = siemens.Siemens(base_path + '/15') svs = data.get_svsdata() svs.save_fida(base_path + '/MEGA1.mat') data = siemens.Siemens(base_path + '/18') svs = data.get_svsdata() svs.save_fida(base_path + '/MEGA2.mat') data = siemens.Siemens(base_path + '/21') svs = data.get_svsdata() svs.save_fida(base_path + '/MEGA3.mat') tform= data.calculate_transform() t1=nib.load(base_path + '/3/104DI-T1w_MPR.nii.gz') img = make_voi.make_voi(t1, tform) nib.save(img, base_path+'/roi.nii.gz')