def insert_hdr_ext(args):
"""Function for anonymising input NIfTI-MRS files.
:param args: Command line arguments parsed in spec2nii.py
:return: List of anonymised images
:rtype: [nib.nifti2.Nifti2Image,]
:return: List of output names
:rtype: [str,]
"""
# Load data
nifti_mrs_img = nib.load(args.file)
with open(args.json_file) as jf:
new_hdr = json.load(jf)
# Make new NIfTI-MRS image
mod_out = nifti_mrs.NIfTI_MRS(
nifti_mrs_img.get_fdata(dtype=nifti_mrs_img.get_data_dtype()),
nifti_mrs_img.affine, nifti_mrs_img.header['pixdim'][4], new_hdr)
# Process output name.
if args.fileout:
fname_out = [
args.fileout,
]
else:
fname_out = [
args.file.with_suffix('').with_suffix('').name,
]
return [
mod_out,
], fname_out
def read_data_list_pair(data_file, list_file, spar_file):
df, num_dict, coord_dict, os_dict = _read_list(list_file)
sorted_data_dict = _read_data(data_file, df)
spar_params = read_spar(spar_file)
# Dwelltime
dwelltime = 1.0 / float(spar_params["sample_frequency"])
# Orientation
affine = _philips_orientation(spar_params)
orientation = NIFTIOrient(affine)
data_out = []
name_out = []
for data_type in sorted_data_dict:
data = sorted_data_dict[data_type]
name = data_type
# Meta
meta = spar_to_nmrs_hdrext(spar_params)
meta.set_standard_def('OriginalFile',
[data_file.name, list_file.name, spar_file.name])
kept_ind = []
for ii, sha in zip(indicies, data.shape[1:]):
if sha > 1:
kept_ind.append(ii)
out_data = data.squeeze()
if len(kept_ind) > 3:
raise TooManyDimError(
'Number of additional dimensions > 3.'
f' Dimensions are {kept_ind} with shape {out_data.shape[1:]}.'
' NIFTI-MRS can only handle three dynamic dimensions. Unsure how to proceed.'
)
unknown_counter = 0
for idx, ki in enumerate(kept_ind):
if ki in defaults:
meta.set_dim_info(idx,
defaults[ki],
info=f'data/list dim {ki}')
else:
meta.set_dim_info(idx,
f'DIM_USER_{unknown_counter}',
info=f'data/list dim {ki}')
unknown_counter += 1
# Insert spatial dimensions
out_data = out_data.reshape((1, 1, 1) + out_data.shape)
# Apply conjugate
out_data = out_data.conj()
data_out.append(
nifti_mrs.NIfTI_MRS(out_data, orientation.Q44, dwelltime, meta))
name_out.append(name)
return data_out, name_out
def _process_svs_pfile(pfile):
'''Handle SVS data
:param Pfile pfile: Pfile object
:return: List of NIFTI MRS data objects
:return: List of file name suffixes
'''
psd = pfile.hdr.rhi_psdname.decode('utf-8').lower()
if psd == 'probe-p':
data, meta, dwelltime, fname_suffix = _process_probe_p(pfile)
elif psd == 'oslaser':
data, meta, dwelltime, fname_suffix = _process_oslaser(pfile)
else:
raise UnsupportedPulseSequenceError(
'Unrecognised sequence, psdname must be "prob-p" or "oslaser".')
orientation = NIFTIOrient(_calculate_affine(pfile))
out_nmrs = []
for dd, mm in zip(data, meta):
out_nmrs.append(nifti_mrs.NIfTI_MRS(dd, orientation.Q44, dwelltime,
mm))
return out_nmrs, fname_suffix
def read_sdat_spar_pair(sdat_file, spar_file, tag=None):
spar_params = read_spar(spar_file)
data = read_sdat(sdat_file, spar_params['samples'], spar_params['rows'])
if data.ndim < 4:
data = data.reshape((1, 1, 1) + data.shape)
# Move to right handed frame
data = data.conj()
# Dwelltime
dwelltime = 1.0 / float(spar_params["sample_frequency"])
# Meta
meta = spar_to_nmrs_hdrext(spar_params)
meta.set_standard_def('OriginalFile', [sdat_file.name])
if tag is not None:
meta.set_dim_info(0, tag)
# Orientation
if spar_params["volume_selection_enable"] == "yes":
affine = _philips_orientation(spar_params)
else:
# Use default affine
affine = np.diag(np.array([10000, 10000, 10000, 1]))
orientation = NIFTIOrient(affine)
return [
nifti_mrs.NIfTI_MRS(data, orientation.Q44, dwelltime, meta),
]
def read_bruker(args):
"""
:param args:
:return list imageOut:
:return list fileoutNames:
"""
imageOut = []
fileoutNames = []
# for all Bruker datasets compliant all queries
for data, properties in yield_bruker(args):
orientation = NIFTIOrient(np.reshape(np.array(properties['affine']), (4,4)))
imageOut.append(
nifti_mrs.NIfTI_MRS(data,
orientation.Q44,
properties['dwell_s'],
nifti_mrs.hdr_ext(
properties['SpectrometerFrequency'],
properties['ResonantNucleus']
))
)
fileoutNames.append(properties['id'])
return imageOut, fileoutNames
def _process_mrsi_pfile(pfile):
'''Handle MRSI data
:param Pfile pfile: Pfile object
:return: List of NIFTI MRS data objects
:return: List of file name suffixes
'''
psd = pfile.hdr.rhi_psdname.decode('utf-8').lower()
if psd != 'probe-p':
raise UnsupportedPulseSequenceError('Unrecognised sequence, psdname must be "probe-p".')
warn('The interpretation of pfile CSI data is poorly tested; rotations or transpositions of the'
' CSI grid could be present. Spec2nii currently lacks a complete set of CSI test data.'
' Please get in touch to help solve this issue!')
data = np.transpose(pfile.map.raw_data, [0, 1, 2, 5, 4, 3])
if data.shape[5] == 1:
data = data.squeeze(axis=5)
# Perform fft
def fft_and_shift(x, axis):
return np.fft.fftshift(np.fft.fft(x, axis=axis), axes=axis)
data = fft_and_shift(data, 0)
data = fft_and_shift(data, 1)
data = fft_and_shift(data, 2)
dwelltime = 1 / pfile.hdr.rhr_spectral_width
meta = _populate_metadata(pfile)
orientation = NIFTIOrient(_calculate_affine_mrsi(pfile))
return [nifti_mrs.NIfTI_MRS(data, orientation.Q44, dwelltime, meta), ], ['', ]
def jmrui_mrui(args):
'''Process .mrui format files.'''
data, header, str_info = read_mrui(args.file)
newshape = (1, 1, 1) + data.shape
data = data.reshape(newshape)
# meta-data
dwelltime = header['sampling_interval'] * 1E-3
meta = jmrui_hdr_to_obj_mrui(header, str_info)
meta.set_standard_def('OriginalFile', [
args.file.name,
])
if data.ndim > 4:
meta.set_dim_info(0, 'DIM_USER_0', info='jMRUI frames')
# Read optional affine file
if args.affine:
affine = np.loadtxt(args.affine)
else:
tmp = np.array([10000, 10000, 10000, 1])
affine = np.diag(tmp)
nifti_orientation = NIFTIOrient(affine)
img_out = [
nifti_mrs.NIfTI_MRS(data, nifti_orientation.Q44, dwelltime, meta),
]
# File names
if args.fileout:
fname_out = [
args.fileout,
]
else:
fname_out = [
args.file.stem,
]
# Place in data output format
return img_out, fname_out
def lcm_raw(args):
'''Processing for LCModel .RAW (and .H2O) files.
Currently only handles one FID per file.
'''
# Read data from file
data, header = readLCModelRaw(args.file, conjugate=True)
newshape = (1, 1, 1) + data.shape
data = data.reshape(newshape)
# meta
dwelltime = header['dwelltime']
meta = nifti_mrs.hdr_ext(header['centralFrequency'],
args.nucleus)
meta.set_standard_def('ConversionMethod', f'spec2nii v{spec2nii_ver}')
conversion_time = datetime.now().isoformat(sep='T', timespec='milliseconds')
meta.set_standard_def('ConversionTime', conversion_time)
meta.set_standard_def('OriginalFile', [basename(args.file), ])
# Read optional affine file
if args.affine:
affine = np.loadtxt(args.affine)
else:
tmp = np.array([10000, 10000, 10000, 1])
affine = np.diag(tmp)
nifti_orientation = NIFTIOrient(affine)
img_out = [nifti_mrs.NIfTI_MRS(data,
nifti_orientation.Q44,
dwelltime,
meta), ]
# File names
if args.fileout:
fname_out = [args.fileout, ]
else:
fname_out = [splitext(basename(args.file))[0], ]
# Place in data output format
return img_out, fname_out
def text(args):
'''Processing for simple ascii formatted columns of data.'''
# Read text from file
data = np.loadtxt(args.file)
data = data[:, 0] + 1j * data[:, 1]
newshape = (1, 1, 1) + data.shape
data = data.reshape(newshape)
# Interpret required arguments (frequency and bandwidth)
dwelltime = 1.0 / args.bandwidth
meta = nifti_mrs.hdr_ext(args.imagingfreq,
args.nucleus)
meta.set_standard_def('ConversionMethod', 'spec2nii')
conversion_time = datetime.now().isoformat(sep='T', timespec='milliseconds')
meta.set_standard_def('ConversionTime', conversion_time)
meta.set_standard_def('OriginalFile', [basename(args.file), ])
# Read optional affine file
if args.affine:
affine = np.loadtxt(args.affine)
else:
tmp = np.array([10000, 10000, 10000, 1])
affine = np.diag(tmp)
nifti_orientation = NIFTIOrient(affine)
img_out = [nifti_mrs.NIfTI_MRS(data,
nifti_orientation.Q44,
dwelltime,
meta), ]
# File names
if args.fileout:
fname_out = [args.fileout, ]
else:
fname_out = [splitext(basename(args.file))[0], ]
# Place in data output format
return img_out, fname_out
def read_bruker(args):
"""
:param args:
:return list imageOut:
:return list fileoutNames:
"""
imageOut = []
fileoutNames = []
# for all Bruker datasets compliant all queries
for data, orientation, dwelltime, meta, name in yield_bruker(args):
imageOut.append(
nifti_mrs.NIfTI_MRS(data,
orientation.Q44,
dwelltime,
meta)
)
fileoutNames.append(name)
return imageOut, fileoutNames
def assemble_nifti_mrs(data, dwellTime, orientation, meta_obj, dim_tags):
for idx, dt in zip(range(data.ndim - 4), dim_tags):
meta_obj.set_dim_info(idx, dt)
return nifti_mrs.NIfTI_MRS(data, orientation.Q44, dwellTime, meta_obj)
def multi_file_dicom(files_in, fname_out, tag, verbose):
"""Parse a list of Siemens DICOM files"""
# Convert each file (combine after)
data_list = []
orientation_list = []
dwelltime_list = []
meta_list = []
series_num = []
inst_num = []
reference = []
str_suffix = []
mainStr = ''
for idx, fn in enumerate(files_in):
if verbose:
print(f'Converting dicom file {fn}')
img = nibabel.nicom.dicomwrappers.wrapper_from_file(fn)
mrs_type = svs_or_CSI(img)
if mrs_type == 'SVS':
specDataCmplx, orientation, dwelltime, meta_obj = process_siemens_svs(
img, verbose=verbose)
newshape = (1, 1, 1) + specDataCmplx.shape
specDataCmplx = specDataCmplx.reshape(newshape)
else:
specDataCmplx, orientation, dwelltime, meta_obj = process_siemens_csi(
img, verbose=verbose)
data_list.append(specDataCmplx)
orientation_list.append(orientation)
dwelltime_list.append(dwelltime)
meta_list.append(meta_obj)
series_num.append(int(img.dcm_data.SeriesNumber))
inst_num.append(int(img.dcm_data.InstanceNumber))
ref_ind, str_suf = identify_integrated_references(
img, img.dcm_data.InstanceNumber)
reference.append(ref_ind)
str_suffix.append(str_suf)
if idx == 0:
if fname_out:
mainStr = fname_out
elif 'SeriesDescription' in img.dcm_data:
mainStr = img.dcm_data.SeriesDescription
elif 'SeriesInstanceUID' in img.dcm_data:
mainStr = img.dcm_data.SeriesInstanceUID
else:
raise missingTagError(
"Neither SeriesDescription or SeriesInstanceUID tags defined."
" Please specify an output filename using '-f'")
# Sort by series and instance number
data_list = np.asarray(data_list)
orientation_list = np.asarray(orientation_list)
dwelltime_list = np.asarray(dwelltime_list)
meta_list = np.asarray(meta_list)
series_num = np.asarray(series_num)
inst_num = np.asarray(inst_num)
reference = np.asarray(reference)
str_suffix = np.asarray(str_suffix)
files_in = np.asarray(files_in)
sort_index = np.lexsort((inst_num, series_num)) # Sort by series then inst
data_list = data_list[sort_index, :]
orientation_list = orientation_list[sort_index]
dwelltime_list = dwelltime_list[sort_index]
meta_list = meta_list[sort_index]
series_num = series_num[sort_index]
inst_num = inst_num[sort_index]
reference = reference[sort_index]
str_suffix = str_suffix[sort_index]
files_in = files_in[sort_index]
group_ind = []
for sn in np.unique(series_num):
for rn in np.unique(reference):
group_ind.append(
list(
np.where(np.logical_and(series_num == sn,
reference == rn))[0]))
if verbose:
print(f'Sorted series numbers: {series_num}')
print(f'Sorted instance numbers: {inst_num}')
print(f'Sorted reference index: {reference}')
print(f'Output groups: {group_ind}')
nifti_mrs_out, fnames_out = [], []
for idx, gr in enumerate(group_ind):
# If data shape, orientation, dwelltime match then
# proceed
def not_equal(lst):
return lst[:-1] != lst[1:]
if not_equal([d.shape for d in data_list[gr]])\
and not_equal([o.Q44.tolist() for o in orientation_list[gr]])\
and not_equal(dwelltime_list[gr]):
raise inconsistentDataError(
'Shape, orientation and dwelltime must match in combined data.'
)
fnames_out.append(mainStr + str_suffix[gr[0]])
dt_used = dwelltime_list[gr[0]]
or_used = orientation_list[gr[0]]
# Add original files to nifti meta information.
meta_used = meta_list[gr[0]]
meta_used.set_standard_def('OriginalFile',
[str(ff) for ff in files_in[gr]])
# Combine data into 5th dimension if needed
data_in_gr = data_list[gr]
if len(data_in_gr) > 1:
combined_data = np.stack(data_in_gr, axis=-1)
else:
combined_data = data_in_gr[0]
# Add dimension information (if not None for default)
if tag:
meta_used.set_dim_info(0, tag)
# Create NIFTI MRS object.
try:
nifti_mrs_out.append(
nifti_mrs.NIfTI_MRS(combined_data, or_used.Q44, dt_used,
meta_used))
except np.linalg.LinAlgError:
warnings.warn("""The quartenion passes to NIfTI_MRS was singular.
Most likely your slice position is not well defined. I have set it to None."""
)
nifti_mrs_out.append(
nifti_mrs.NIfTI_MRS(combined_data, None, dt_used, meta_used))
# If there are any identical names then append an index
seen = np.unique(fnames_out)
if seen.size < len(fnames_out):
seen_count = np.zeros(seen.shape, dtype=int)
fnames_out_checked = []
for fn in fnames_out:
if fn in seen:
seen_index = seen == fn
fnames_out_checked.append(fn +
f'_{seen_count[seen_index][0]:03}')
seen_count[seen_index] += 1
else:
fnames_out_checked.append(fn)
return nifti_mrs_out, fnames_out_checked
else:
return nifti_mrs_out, fnames_out
def read_varian(args):
"""read_varian -- load a varian fid.
Note that this format is very flexible and some rather large assumptions are made.
At the moment, this assumes little.
:param file: path to the varian .fid directory, containing fid and procpar files.
returns img_out, fname_out
"""
dic, data = v.read(args.file)
# extract number of coils
number_of_coils = 0
for i in dic['procpar']['rcvrs']['values'][0]:
if re.search(i, 'y'):
number_of_coils += 1
# number of time points -- probably
number_of_time_points = float(
dic['procpar']['arraydim']['values'][0]) / number_of_coils
if (not number_of_time_points.is_integer()):
raise ValueError('Coil reshaping failed')
number_of_time_points = int(number_of_time_points)
# spectral number of points
number_of_spectral_points = int(int(dic['np']) / 2)
# reshape
newshape = (1, 1, 1, number_of_spectral_points, number_of_coils,
number_of_time_points)
data = data.transpose()
data = np.resize(data, newshape)
# extract additional spectral metadata
dwelltime = 1.0 / float(dic['procpar']['sw']['values'][0])
imagingfreq = float(dic['procpar']['sfrq']['values'][0])
nucleus = dic['procpar']['tn']['values'][0]
# reshape to be in the form '13C' rather than 'C13'
nucleus = nucleus[1:] + nucleus[0]
try:
repitition_time = float(dic['procpar']['tr']['values'][0])
except KeyError:
pass
try:
echotime = float(
dic['procpar']['te']['values'][0]) # In ms if 'tis there
except KeyError:
pass
try:
echotime = float(dic['procpar']['pw']['values'][0]) + float(
dic['procpar']['alfa']['values'][0])
except KeyError:
pass
try:
echotime = float(dic['procpar']['p1']['values'][0]) + float(
dic['procpar']['alfa']['values'][0])
except KeyError:
pass
# Parse 3D localisation
sequence_name = dic['procpar']['seqfil']['values'][0]
if (sequence_name.count('press') or sequence_name.count('steam')):
affine = _varian_orientation_3d(dic)
else:
affine = np.diag(np.array([10000, 10000, 10000,
1])) # 10 m slab for now....
# TODO: Jack should implement the affine matrix correctly for all sequences
orientation = NIFTIOrient(affine)
# create object
meta = nifti_mrs.hdr_ext(imagingfreq, nucleus)
meta.set_standard_def('ConversionMethod', f'spec2nii v{spec2nii_ver}')
meta.set_standard_def('EchoTime', echotime)
meta.set_standard_def('RepetitionTime', repitition_time)
meta.set_standard_def('Manufacturer', 'Varian')
meta.set_standard_def('ProtocolName',
dic['procpar']['seqfil']['values'][0])
meta.set_standard_def('PatientName',
dic['procpar']['comment']['values'][0])
# stuff that is nice to have:
try:
meta.set_standard_def('SoftwareVersions',
dic['procpar']['parver']['values'][0])
meta.set_standard_def('TxCoil', dic['procpar']['rfcoil']['values'][0])
meta.set_standard_def('RxCoil', dic['procpar']['rfcoil']['values'][0])
except KeyError:
warnings.warn('Expected standard metadata keying failed')
try:
meta.set_standard_def('InversionTime',
dic['procpar']['ti']['values'][0])
except KeyError:
pass
try:
meta.set_standard_def('ExcitationFlipAngle',
dic['procpar']['flip1']['values'][0])
except KeyError:
pass
conversion_time = datetime.now().isoformat(sep='T',
timespec='milliseconds')
meta.set_standard_def('ConversionTime', conversion_time)
meta.set_standard_def('OriginalFile', [basename(args.file)])
# k-space
meta.set_standard_def('kSpace', [False, False, False])
# set tag dimensions
meta.set_dim_info(0, "DIM_COIL")
meta.set_dim_info(1, args.tag6)
# Stuff full headers into user fields
if args.dump_headers:
meta.set_user_def(key='VarianProcpar',
doc='Varian procpar metadata.',
value=dic['procpar'])
# File names
if args.fileout:
fname_out = [
args.fileout,
]
else:
fname_out = [
splitext(basename(args.file))[0],
]
return [
nifti_mrs.NIfTI_MRS(data, orientation.Q44, dwelltime, meta),
], fname_out
def multi_file_dicom(files_in, fname_out, tag, verbose):
"""Parse a list of UIH DICOM files"""
# Convert each file (combine after)
data_list = []
ref_list = []
orientation_list = []
dwelltime_list = []
meta_list = []
meta_ref_list = []
mainStr = ''
for idx, fn in enumerate(files_in):
if verbose:
print(f'Converting dicom file {fn}')
img = nibabel.nicom.dicomwrappers.wrapper_from_file(fn)
mrs_type = svs_or_CSI(img)
if mrs_type == 'SVS':
specDataCmplx, spec_ref, orientation, dwelltime, meta_obj, meta_ref_obj = _process_philips_svs(
img, verbose)
newshape = (1, 1, 1) + specDataCmplx.shape
spec_data = specDataCmplx.reshape(newshape)
spec_ref = spec_ref.reshape(newshape)
# Data appears to require conjugation to meet standard's conventions.
spec_data = spec_data.conj()
spec_ref = spec_ref.conj()
elif mrs_type == 'FID':
specDataCmplx, spec_ref, orientation, dwelltime, meta_obj, meta_ref_obj = _process_philips_fid(
img, verbose)
newshape = (1, 1, 1) + specDataCmplx.shape
spec_data = specDataCmplx.reshape(newshape)
spec_ref = spec_ref.reshape(newshape)
# Data appears to require conjugation to meet standard's conventions.
spec_data = spec_data.conj()
spec_ref = spec_ref.conj()
else:
raise CSINotHandledError(
'CSI data is currently not handled for the Philips DICOM format.'
'Please contact the developers if you have examples of this type of data.'
)
data_list.append(spec_data)
ref_list.append(spec_ref)
orientation_list.append(orientation)
dwelltime_list.append(dwelltime)
meta_list.append(meta_obj)
meta_ref_list.append(meta_ref_obj)
if idx == 0:
if fname_out:
mainStr = fname_out
else:
mainStr = img.dcm_data.SeriesDescription
# If data shape, orientation and dwelltime match combine
# into one NIFTI MRS object.
# Otherwise return a list of files/names
def all_equal(lst):
return lst[:-1] == lst[1:]
combine = all_equal([d.shape for d in data_list])\
and all_equal([o.Q44.tolist() for o in orientation_list])\
and all_equal(dwelltime_list)
nifti_mrs_out, fnames_out = [], []
if combine:
# Combine files into single MRS NIfTI
# Single file name
fnames_out.append(mainStr)
fnames_out.append(mainStr + '_ref')
dt_used = dwelltime_list[0]
or_used = orientation_list[0]
# Add original files to nifti meta information.
meta_used = meta_list[0]
meta_used.set_standard_def('OriginalFile',
[str(ff.name) for ff in files_in])
meta_ref_used = meta_ref_list[0]
meta_ref_used.set_standard_def('OriginalFile',
[str(ff.name) for ff in files_in])
# Combine data into 5th dimension if needed
if len(data_list) > 1:
combined_data = np.stack(data_list, axis=-1)
combined_ref = np.stack(ref_list, axis=-1)
else:
combined_data = data_list[0]
combined_ref = ref_list[0]
# Add dimension information (if not None for default)
if tag:
meta_used.set_dim_info(0, tag)
# Create NIFTI MRS object.
nifti_mrs_out.append(
nifti_mrs.NIfTI_MRS(combined_data, or_used.Q44, dt_used,
meta_used))
nifti_mrs_out.append(
nifti_mrs.NIfTI_MRS(combined_ref, or_used.Q44, dt_used,
meta_ref_used))
else:
for idx, (dd, rr, oo, dt, mm, mr, ff) in enumerate(
zip(data_list, ref_list, orientation_list, dwelltime_list,
meta_list, meta_ref_list, files_in)):
# Add original files to nifti meta information.
mm.set_standard_def('OriginalFile', [
str(ff.name),
])
fnames_out.append(f'{mainStr}_{idx:03}')
nifti_mrs_out.append(nifti_mrs.NIfTI_MRS(dd, oo.Q44, dt, mm))
mr.set_standard_def('OriginalFile', [
str(ff.name),
])
fnames_out.append(f'{mainStr}_ref_{idx:03}')
nifti_mrs_out.append(nifti_mrs.NIfTI_MRS(rr, oo.Q44, dt, mr))
return nifti_mrs_out, fnames_out
def multi_file_dicom(files_in, fname_out, tag, verbose):
"""Parse a list of UIH DICOM files"""
# Convert each file (combine after)
data_list = []
orientation_list = []
dwelltime_list = []
meta_list = []
mainStr = ''
for idx, fn in enumerate(files_in):
if verbose:
print(f'Converting dicom file {fn}')
img = nibabel.nicom.dicomwrappers.wrapper_from_file(fn)
mrs_type = svs_or_CSI(img)
if mrs_type == 'SVS':
specDataCmplx, orientation, dwelltime, meta_obj = process_uih_svs(img, verbose)
newshape = (1, 1, 1) + specDataCmplx.shape
specDataCmplx = specDataCmplx.reshape(newshape)
else:
specDataCmplx, orientation, dwelltime, meta_obj = process_uih_csi(img, verbose)
data_list.append(specDataCmplx)
orientation_list.append(orientation)
dwelltime_list.append(dwelltime)
meta_list.append(meta_obj)
if idx == 0:
if fname_out:
mainStr = fname_out
else:
mainStr = img.dcm_data.SeriesDescription
# If data shape, orientation and dwelltime match combine
# into one NIFTI MRS object.
# Otherwise return a list of files/names
def all_equal(lst):
return lst[:-1] == lst[1:]
combine = all_equal([d.shape for d in data_list])\
and all_equal([o.Q44.tolist() for o in orientation_list])\
and all_equal(dwelltime_list)
nifti_mrs_out, fnames_out = [], []
if combine:
# Combine files into single MRS NIfTI
# Single file name
fnames_out.append(mainStr)
dt_used = dwelltime_list[0]
or_used = orientation_list[0]
# Add original files to nifti meta information.
meta_used = meta_list[0]
meta_used.set_standard_def('OriginalFile', [str(ff.name) for ff in files_in])
# Combine data into 5th dimension if needed
if len(data_list) > 1:
combined_data = np.stack(data_list, axis=-1)
else:
combined_data = data_list[0]
# Add dimension information (if not None for default)
if tag:
meta_used.set_dim_info(0, tag)
# Create NIFTI MRS object.
nifti_mrs_out.append(nifti_mrs.NIfTI_MRS(combined_data, or_used.Q44, dt_used, meta_used))
else:
for idx, (dd, oo, dt, mm, ff) in enumerate(zip(data_list,
orientation_list,
dwelltime_list,
meta_list,
files_in)):
# Add original files to nifti meta information.
mm.set_standard_def('OriginalFile', [str(ff.name), ])
fnames_out.append(f'{mainStr}_{idx:03}')
nifti_mrs_out.append(nifti_mrs.NIfTI_MRS(dd, oo.Q44, dt, mm))
return nifti_mrs_out, fnames_out
