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_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_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 _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_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 _proc_dataset(d, args):
"""
Yield data and properties of a single dataset
"""
# merge 2dseq complex frame group if present
if d.is_complex and d.type == '2dseq':
d = FrameGroupMerger().merge(d, 'FG_COMPLEX')
# prepare the data array
if d.is_svs:
data = _prep_data_svs(d)
elif d.is_mrsi:
data = _prep_data_mrsi(d)
else:
data = d.data
# get properties
properties = d.to_dict()
# Orientation information
if d.type == 'fid':
orientation = NIFTIOrient(_fid_affine_from_params(d))
else:
orientation = NIFTIOrient(np.reshape(np.array(properties['affine']), (4, 4)))
# Meta data
if d.type == 'fid':
meta = _fid_meta(d, dump=args.dump_headers)
else:
meta = _2dseq_meta(d, dump=args.dump_headers)
# Dwelltime - to do resolve this factor of 2 issue
if d.type == 'fid':
dwelltime = d.dwell_s * 2
else:
dwelltime = d.dwell_s * 2
if args.fileout:
name = args.fileout + '_' + d.id.rstrip('_')
else:
name = d.id.rstrip('_')
yield data, orientation, dwelltime, meta, name
def process_uih_csi(img, verbose):
"""Process UIH DICOM MRSI data"""
specData = np.frombuffer(img.dcm_data[('5600', '0020')].value, dtype=np.single)
specDataCmplx = specData[0::2] + 1j * specData[1::2]
warn('The orientation of UIH MRSI is relatively untested.'
' Please contribute data to help fix this!')
shape = (img.dcm_data.Columns,
img.dcm_data.Rows,
int(img.dcm_data.NumberOfFrames),
img.dcm_data.SpectroscopyAcquisitionDataColumns)
specDataCmplx = specDataCmplx.reshape(shape)
# WTC 05/01/21 From the limited test data I have I think there is this transposition.
specDataCmplx = np.swapaxes(specDataCmplx, 0, 1)
# Extract dicom parameters
imageOrientationPatient = img.image_orient_patient.T
imagePositionPatient = img.image_position
xyzMM = np.asarray(img.voxel_sizes)
currNiftiOrientation = dcm_to_nifti_orientation(imageOrientationPatient,
imagePositionPatient,
xyzMM,
specDataCmplx.shape[:3],
half_shift=True,
verbose=verbose)
# UIH specific tweaks
Q44 = currNiftiOrientation.Q44
# 1) negate 3rd direction if ('0065', 'ff06') 3rd element is negative
if float(img.dcm_data[('0065', 'ff06')].value[2]) < 0.0:
Q44[:3, :3] = Q44[:3, :3] @ np.array([[1, 0, 0], [0, 1, 0], [0, 0, -1]])
# 2) A half voxel shift is needed in the third dimension
v = np.asarray([0, 0, 0.5]) @ Q44[:3, :3].T
Q44[:3, 3] += v
currNiftiOrientation = NIFTIOrient(Q44)
dwelltime = 1.0 / img.dcm_data.SpectralWidth
meta = extractDicomMetadata(img)
return specDataCmplx, currNiftiOrientation, 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 dcm_to_nifti_orientation(imageOrientationPatient,
imagePositionPatient,
xyzMM,
data_shape,
half_shift=False,
verbose=False):
"""Convert DCM orientation parameters to nifti orientations.
:arg numpy.ndarray imageOrientationPatient: DICOM imageOrientationPatient tag. Shape = 2,3.
:arg numpy.ndarray imagePositionPatient: DICOM imagePositionPatient tag.
:arg numpy.ndarray xyzMM: Array containing the voxel sizes as contained in the
nibabel.nicom.dicomwrappers.Wrapper voxel_sizes property.
:arg data_shape: Shape of the spatial dimensions (dimensions 0-2).
:arg bool half_shift: Apply half voxel shift to the x and y dimension.
:arg bool verbose: Print debugging output.
:return: NIFTIOrient object.
:rtype: NIFTIOrient
"""
# in style of dcm2niix
# 1) calculate Q44
Q44 = nifti_dicom2mat(imageOrientationPatient,
imagePositionPatient,
xyzMM,
verbose=verbose)
# 2) calculate nifti quaternion parameters
# From github.com/rordenlab/dcm2niix/blob/
# 081c6300d0cf47088f0873cd586c9745498f637a/console/nii_dicom.cpp#L604
_, Q44 = verify_slice_dir(Q44,
data_shape,
imagePositionPatient,
verbose=verbose)
Q44[:2, :] *= -1
# 3) If required apply the half-voxel shift in the first two dimensions
if half_shift:
Q44 = apply_half_voxel_shift(Q44)
if verbose:
print(f'Final Q44:\n {Q44}')
# 4) place in data class for nifti orientation parameters
return NIFTIOrient(Q44)
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 _process_philips_fid(img, verbose):
"""Process Philips DICOM FID data"""
specData = np.frombuffer(img.dcm_data[('5600', '0020')].value,
dtype=np.single)
specDataCmplx = specData[0::2] + 1j * specData[1::2]
# In the one piece of data I have been provided the data is twice as long as indicated (1 avg)
# and the second half is a water reference.
spec_points = img.dcm_data.SpectroscopyAcquisitionDataColumns
spec_data_main = specDataCmplx[:spec_points]
spec_data_ref = specDataCmplx[spec_points:]
# 1) Extract dicom parameters
defaultaffine = np.diag(np.array([10000, 10000, 10000, 1]))
currNiftiOrientation = NIFTIOrient(defaultaffine)
dwelltime = 1.0 / img.dcm_data.SpectralWidth
meta = _extractDicomMetadata(img)
meta_r = _extractDicomMetadata(img, water_suppressed=False)
return spec_data_main, spec_data_ref, currNiftiOrientation, dwelltime, meta, meta_r
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