def get_bvecs_bvals(self):
tensor_file = os.path.join(self.dirpath,
'_' + self.basename + '_tensor.dat')
if not os.path.exists(tensor_file):
log.warning('tensor file not found!')
self.bvecs = None
self.bvals = None
return
with open(tensor_file) as fp:
try:
uid = fp.readline().rstrip()
ndirs = int('0' + fp.readline().rstrip())
except:
fp.seek(0, 0)
ndirs = int('0' + fp.readline().rstrip())
bvecs = np.fromfile(fp, sep=' ')
if ndirs != self.dwi_numdirs or self.dwi_numdirs != bvecs.size / 3.:
log.warning(
'tensor file numdirs does not match PFile header numdirs!')
self.bvecs = None
self.bvals = None
else:
num_nondwi = self.num_timepoints_available - self.dwi_numdirs
bvals = np.concatenate((np.zeros(num_nondwi, dtype=float),
np.tile(self.dwi_bvalue,
self.dwi_numdirs)))
bvecs = np.hstack(
(np.zeros((3, num_nondwi),
dtype=float), bvecs.reshape(self.dwi_numdirs, 3).T))
self.bvecs, self.bvals = nimsmrdata.adjust_bvecs(
bvecs, bvals, self.scanner_type, self.image_rotation)
def get_bvecs_bvals(self):
tensor_file = os.path.join(self.dirpath, '_'+self.basename+'_tensor.dat')
if not os.path.exists(tensor_file):
log.warning('tensor file not found!')
self.bvecs = None
self.bvals = None
return
with open(tensor_file) as fp:
uid = fp.readline().rstrip()
ndirs = int('0'+fp.readline().rstrip())
bvecs = np.fromfile(fp, sep=' ')
if uid != self._hdr.series.series_uid:
raise NIMSPFileError('tensor file UID does not match PFile UID!')
if ndirs != self.dwi_numdirs or self.dwi_numdirs != bvecs.size / 3.:
log.warning('tensor file numdirs does not match PFile header numdirs!')
self.bvecs = None
self.bvals = None
else:
num_nondwi = self.num_timepoints_available - self.dwi_numdirs # FIXME: assumes that all the non-dwi images are acquired first.
bvals = np.concatenate((np.zeros(num_nondwi, dtype=float), np.tile(self.dwi_bvalue, self.dwi_numdirs)))
bvecs = np.hstack((np.zeros((3,num_nondwi), dtype=float), bvecs.reshape(self.dwi_numdirs, 3).T))
self.bvecs,self.bvals = nimsmrdata.adjust_bvecs(bvecs, bvals, self.scanner_type, self.image_rotation)
def get_bvecs_bvals(self):
tensor_file = os.path.join(self.dirpath, '_'+self.basename+'_tensor.dat')
if not os.path.exists(tensor_file):
log.warning('tensor file not found!')
self.bvecs = None
self.bvals = None
return
with open(tensor_file) as fp:
try:
uid = fp.readline().rstrip()
ndirs = int('0'+fp.readline().rstrip())
except:
fp.seek(0, 0)
ndirs = int('0'+fp.readline().rstrip())
bvecs = np.fromfile(fp, sep=' ')
if ndirs != self.dwi_numdirs or self.dwi_numdirs != bvecs.size / 3.:
log.warning('tensor file numdirs does not match PFile header numdirs!')
self.bvecs = None
self.bvals = None
else:
num_nondwi = self.num_timepoints_available - self.dwi_numdirs
bvals = np.concatenate((np.zeros(num_nondwi, dtype=float), np.tile(self.dwi_bvalue, self.dwi_numdirs)))
bvecs = np.hstack((np.zeros((3,num_nondwi), dtype=float), bvecs.reshape(self.dwi_numdirs, 3).T))
self.bvecs,self.bvals = nimsmrdata.adjust_bvecs(bvecs, bvals, self.scanner_type, self.image_rotation)
def load_all_metadata(self):
# TODO: make this less expensive. We can probably get away with a much more selective
# loading of dicoms. E.g, maybe just the first volume and then the first slice of each subsequent vol?
if self.dcm_list == None:
self.load_dicoms()
if self.is_dwi:
self.bvals = np.array([getelem(dcm, TAG_BVALUE, float)[0] for dcm in self.dcm_list[0::self.num_slices]])
self.bvecs = np.array([[getelem(dcm, TAG_BVEC[i], float) for i in range(3)] for dcm in self.dcm_list[0::self.num_slices]]).transpose()
# Try to carry on on incomplete datasets. Also, some weird scans like MRVs don't set the
# number of slices correctly in the dicom header. (Or at least they set it in a weird way
# that we don't understand.)
if len(self.dcm_list) < self.num_slices:
self.num_slices = len(self.dcm_list)
if len(self.dcm_list) < self.total_num_slices:
self.total_num_slices = len(self.dcm_list)
image_position = [tuple(getelem(dcm, 'ImagePositionPatient', float, [0., 0., 0.])) for dcm in self.dcm_list]
if self.num_timepoints == 1:
unique_slice_pos = np.unique(image_position).astype(np.float)
# crude check for a 3-plane localizer. When we get one of these, we actually
# want each plane to be a different time point.
slice_distance = np.sqrt((np.diff(unique_slice_pos,axis=0)**2).sum(1))
# ugly hack! The number of time points is the number of big (>10mm) jumps in slice-to-slice distance.
self.num_timepoints = np.sum((slice_distance - np.median(slice_distance)) > 10) + 1
self.num_slices = self.total_num_slices / self.num_timepoints
cosines = getelem(self._hdr, 'ImageOrientationPatient', float, 6 * [np.nan])
row_cosines = np.array(cosines[0:3])
col_cosines = np.array(cosines[3:6])
# Compute the slice_norm. From the NIFTI-1 header:
# The third column of R will be either the cross-product of the first 2 columns or
# its negative. It is possible to infer the sign of the 3rd column by examining
# the coordinates in DICOM attribute (0020,0032) "Image Position (Patient)" for
# successive slices. However, this method occasionally fails for reasons that I
# (RW Cox) do not understand.
# For Siemens data, it seems that looking at 'SliceNormalVector' can help resolve this.
# dicom_slice_norm = getelem(self._hdr, 'SliceNormalVector', float, None)
# if dicom_slice_norm != None and np.dot(self.slice_norm, dicom_slice_norm) < 0.:
# self.slice_norm = -self.slice_norm
# But otherwise, we'll have to fix this up after we load all the dicoms and check
# the slice-to-slice position deltas.
slice_norm = np.cross(row_cosines, col_cosines)
# FIXME: the following could fail if the acquisition was before a full volume was aquired.
if np.dot(slice_norm, image_position[0]) > np.dot(slice_norm, image_position[self.num_slices-1]):
log.debug('flipping slice order')
#slice_norm = -slice_norm
self.reverse_slice_order = True
self.origin = image_position[self.num_slices-1] * np.array([-1, -1, 1])
else:
self.origin = image_position[0] * np.array([-1, -1, 1])
self.slice_order = nimsmrdata.SLICE_ORDER_UNKNOWN
if self.total_num_slices >= self.num_slices and getelem(self.dcm_list[0], 'TriggerTime', float) is not None:
trigger_times = np.array([getelem(dcm, 'TriggerTime', float) for dcm in self.dcm_list[0:self.num_slices]])
if self.reverse_slice_order:
# the slice order will be flipped when the image is saved, so flip the trigger times
trigger_times = trigger_times[::-1]
trigger_times_from_first_slice = trigger_times[0] - trigger_times
if self.num_slices > 2:
self.slice_duration = float(min(abs(trigger_times_from_first_slice[1:]))) / 1000. # msec to sec
if trigger_times_from_first_slice[1] < 0:
# slice 1 happened after slice 0, so this must be either SEQ_INC or ALT_INC
self.slice_order = nimsmrdata.SLICE_ORDER_SEQ_INC if trigger_times[2] > trigger_times[1] else nimsmrdata.SLICE_ORDER_ALT_INC
else:
# slice 1 before slice 0, so must be ALT_DEC or SEQ_DEC
self.slice_order = nimsmrdata.SLICE_ORDER_ALT_DEC if trigger_times[2] > trigger_times[1] else nimsmrdata.SLICE_ORDER_SEQ_DEC
else:
self.slice_duration = trigger_times[0]
self.slice_order = nimsmrdata.SLICE_ORDER_SEQ_INC
rot = nimsmrdata.compute_rotation(row_cosines, col_cosines, slice_norm)
if self.is_dwi:
self.bvecs,self.bvals = nimsmrdata.adjust_bvecs(self.bvecs, self.bvals, self.scanner_type, rot)
self.qto_xyz = nimsmrdata.build_affine(rot, self.mm_per_vox, self.origin)
super(NIMSDicom, self).load_all_metadata()
def load_all_metadata(self):
# TODO: make this less expensive. We can probably get away with a much more selective
# loading of dicoms. E.g, maybe just the first volume and then the first slice of each subsequent vol?
if self.dcm_list == None:
self.load_dicoms()
if self.is_dwi:
self.bvals = np.array([
getelem(dcm, TAG_BVALUE, float)[0]
for dcm in self.dcm_list[0::self.num_slices]
])
self.bvecs = np.array(
[[getelem(dcm, TAG_BVEC[i], float) for i in range(3)]
for dcm in self.dcm_list[0::self.num_slices]]).transpose()
# Try to carry on on incomplete datasets. Also, some weird scans like MRVs don't set the
# number of slices correctly in the dicom header. (Or at least they set it in a weird way
# that we don't understand.)
if len(self.dcm_list) < self.num_slices:
self.num_slices = len(self.dcm_list)
if len(self.dcm_list) < self.total_num_slices:
self.total_num_slices = len(self.dcm_list)
image_position = [
tuple(getelem(dcm, 'ImagePositionPatient', float, [0., 0., 0.]))
for dcm in self.dcm_list
]
if self.num_timepoints == 1:
unique_slice_pos = np.unique(image_position).astype(np.float)
# crude check for a 3-plane localizer. When we get one of these, we actually
# want each plane to be a different time point.
slice_distance = np.sqrt((np.diff(unique_slice_pos,
axis=0)**2).sum(1))
# ugly hack! The number of time points is the number of big (>10mm) jumps in slice-to-slice distance.
self.num_timepoints = np.sum(
(slice_distance - np.median(slice_distance)) > 10) + 1
self.num_slices = self.total_num_slices / self.num_timepoints
cosines = getelem(self._hdr, 'ImageOrientationPatient', float,
6 * [np.nan])
row_cosines = np.array(cosines[0:3])
col_cosines = np.array(cosines[3:6])
# Compute the slice_norm. From the NIFTI-1 header:
# The third column of R will be either the cross-product of the first 2 columns or
# its negative. It is possible to infer the sign of the 3rd column by examining
# the coordinates in DICOM attribute (0020,0032) "Image Position (Patient)" for
# successive slices. However, this method occasionally fails for reasons that I
# (RW Cox) do not understand.
# For Siemens data, it seems that looking at 'SliceNormalVector' can help resolve this.
# dicom_slice_norm = getelem(self._hdr, 'SliceNormalVector', float, None)
# if dicom_slice_norm != None and np.dot(self.slice_norm, dicom_slice_norm) < 0.:
# self.slice_norm = -self.slice_norm
# But otherwise, we'll have to fix this up after we load all the dicoms and check
# the slice-to-slice position deltas.
slice_norm = np.cross(row_cosines, col_cosines)
# FIXME: the following could fail if the acquisition was before a full volume was aquired.
if np.dot(slice_norm, image_position[0]) > np.dot(
slice_norm, image_position[self.num_slices - 1]):
log.debug('flipping slice order')
#slice_norm = -slice_norm
self.reverse_slice_order = True
self.origin = image_position[self.num_slices - 1] * np.array(
[-1, -1, 1])
else:
self.origin = image_position[0] * np.array([-1, -1, 1])
self.slice_order = nimsmrdata.SLICE_ORDER_UNKNOWN
if self.total_num_slices >= self.num_slices and getelem(
self.dcm_list[0], 'TriggerTime', float) is not None:
trigger_times = np.array([
getelem(dcm, 'TriggerTime', float)
for dcm in self.dcm_list[0:self.num_slices]
])
if self.reverse_slice_order:
# the slice order will be flipped when the image is saved, so flip the trigger times
trigger_times = trigger_times[::-1]
trigger_times_from_first_slice = trigger_times[0] - trigger_times
if self.num_slices > 2:
self.slice_duration = float(
min(abs(trigger_times_from_first_slice[1:]))
) / 1000. # msec to sec
if trigger_times_from_first_slice[1] < 0:
# slice 1 happened after slice 0, so this must be either SEQ_INC or ALT_INC
self.slice_order = nimsmrdata.SLICE_ORDER_SEQ_INC if trigger_times[
2] > trigger_times[
1] else nimsmrdata.SLICE_ORDER_ALT_INC
else:
# slice 1 before slice 0, so must be ALT_DEC or SEQ_DEC
self.slice_order = nimsmrdata.SLICE_ORDER_ALT_DEC if trigger_times[
2] > trigger_times[
1] else nimsmrdata.SLICE_ORDER_SEQ_DEC
else:
self.slice_duration = trigger_times[0]
self.slice_order = nimsmrdata.SLICE_ORDER_SEQ_INC
rot = nimsmrdata.compute_rotation(row_cosines, col_cosines, slice_norm)
if self.is_dwi:
self.bvecs, self.bvals = nimsmrdata.adjust_bvecs(
self.bvecs, self.bvals, self.scanner_type, rot)
self.qto_xyz = nimsmrdata.build_affine(rot, self.mm_per_vox,
self.origin)
super(NIMSDicom, self).load_all_metadata()
