def _create_bvals_bvecs(multiframe_dicom, bval_file, bvec_file, nifti, nifti_file):
"""
Write the bvals from the sorted dicom files to a bval file
Inspired by https://github.com/IBIC/ibicUtils/blob/master/ibicBvalsBvecs.py
"""
# create the empty arrays
number_of_stack_slices = common.get_ss_value(multiframe_dicom[Tag(0x2001, 0x105f)][0][Tag(0x2001, 0x102d)])
number_of_stacks = int(int(multiframe_dicom.NumberOfFrames) / number_of_stack_slices)
bvals = numpy.zeros([number_of_stacks], dtype=numpy.int32)
bvecs = numpy.zeros([number_of_stacks, 3])
# loop over all timepoints and create a list with all bvals and bvecs
for stack_index in range(0, number_of_stacks):
stack = multiframe_dicom[Tag(0x5200, 0x9230)][stack_index]
if str(stack[Tag(0x0018, 0x9117)][0][Tag(0x0018, 0x9075)].value) == 'DIRECTIONAL':
bvals[stack_index] = common.get_fd_value(stack[Tag(0x0018, 0x9117)][0][Tag(0x0018, 0x9087)])
bvecs[stack_index, :] = common.get_fd_array_value(stack[Tag(0x0018, 0x9117)][0]
[Tag(0x0018, 0x9076)][0][Tag(0x0018, 0x9089)], 3)
# truncate nifti if needed
nifti, bvals, bvecs = _fix_diffusion_images(bvals, bvecs, nifti, nifti_file)
# save the found bvecs to the file
if numpy.count_nonzero(bvals) > 0 or numpy.count_nonzero(bvecs) > 0:
common.write_bval_file(bvals, bval_file)
common.write_bvec_file(bvecs, bvec_file)
else:
bval_file = None
bvec_file = None
bvals = None
bvecs = None
return bvals, bvecs, bval_file, bvec_file
def _create_bvals_bvecs(multiframe_dicom, bval_file, bvec_file, nifti, nifti_file):
"""
Write the bvals from the sorted dicom files to a bval file
Inspired by https://github.com/IBIC/ibicUtils/blob/master/ibicBvalsBvecs.py
"""
# create the empty arrays
number_of_stack_slices = common.get_ss_value(multiframe_dicom[Tag(0x2001, 0x105f)][0][Tag(0x2001, 0x102d)])
number_of_stacks = int(int(multiframe_dicom.NumberOfFrames) / number_of_stack_slices)
bvals = numpy.zeros([number_of_stacks], dtype=numpy.int32)
bvecs = numpy.zeros([number_of_stacks, 3])
# loop over all timepoints and create a list with all bvals and bvecs
for stack_index in range(0, number_of_stacks):
stack = multiframe_dicom[Tag(0x5200, 0x9230)][stack_index]
if str(stack[Tag(0x0018, 0x9117)][0][Tag(0x0018, 0x9075)].value) == 'DIRECTIONAL':
bvals[stack_index] = common.get_fd_value(stack[Tag(0x0018, 0x9117)][0][Tag(0x0018, 0x9087)])
bvecs[stack_index, :] = common.get_fd_array_value(stack[Tag(0x0018, 0x9117)][0]
[Tag(0x0018, 0x9076)][0][Tag(0x0018, 0x9089)], 3)
# truncate nifti if needed
nifti, bvals, bvecs = _fix_diffusion_images(bvals, bvecs, nifti, nifti_file)
# save the found bvecs to the file
if numpy.count_nonzero(bvals) > 0 or numpy.count_nonzero(bvecs) > 0:
common.write_bval_file(bvals, bval_file)
common.write_bvec_file(bvecs, bvec_file)
else:
bval_file = None
bvec_file = None
bvals = None
bvecs = None
return bvals, bvecs, bval_file, bvec_file
def _is_bval_type_b(grouped_dicoms):
"""
Check if the bvals are stored in the second of 2 currently known ways for single frame dti
"""
bval_tag = Tag(0x0018, 0x9087)
bvec_tag = Tag(0x0018, 0x9089)
for group in grouped_dicoms:
if bvec_tag in group[0] and bval_tag in group[0]:
bvec = common.get_fd_array_value(group[0][bvec_tag], 3)
bval = common.get_fd_value(group[0][bval_tag])
if _is_float(bvec[0]) and _is_float(bvec[1]) and _is_float(bvec[2]) and _is_float(bval) and bval != 0:
return True
return False
def _is_bval_type_b(grouped_dicoms):
"""
Check if the bvals are stored in the second of 2 currently known ways for single frame dti
"""
bval_tag = Tag(0x0018, 0x9087)
bvec_tag = Tag(0x0018, 0x9089)
for group in grouped_dicoms:
if bvec_tag in group[0] and bval_tag in group[0]:
bvec = common.get_fd_array_value(group[0][bvec_tag], 3)
bval = common.get_fd_value(group[0][bval_tag])
if _is_float(bvec[0]) and _is_float(bvec[1]) and _is_float(bvec[2]) and _is_float(bval) and bval != 0:
return True
return False
def _create_bvecs(sorted_dicoms, bvec_file):
"""
Calculate the bvecs and write the to a bvec file
# inspired by dicom2nii from mricron
# see http://users.fmrib.ox.ac.uk/~robson/internal/Dicom2Nifti111.m
"""
if type(sorted_dicoms[0]) is list:
dicom_headers = sorted_dicoms[0][0]
else:
dicom_headers = sorted_dicoms[0]
# get the patient orientation
image_orientation = dicom_headers.ImageOrientationPatient
read_vector = numpy.array([float(image_orientation[0]), float(image_orientation[1]), float(image_orientation[2])])
phase_vector = numpy.array([float(image_orientation[3]), float(image_orientation[4]), float(image_orientation[5])])
mosaic_vector = numpy.cross(read_vector, phase_vector)
# normalize the vectors
read_vector /= numpy.linalg.norm(read_vector)
phase_vector /= numpy.linalg.norm(phase_vector)
mosaic_vector /= numpy.linalg.norm(mosaic_vector)
# create an empty array for the new bvecs
bvecs = numpy.zeros([len(sorted_dicoms), 3])
# for each slice calculate the new bvec
for index in range(0, len(sorted_dicoms)):
if type(sorted_dicoms[0]) is list:
dicom_headers = sorted_dicoms[index][0]
else:
dicom_headers = sorted_dicoms[index]
# get the bval als this is needed in some checks
bval = common.get_is_value(dicom_headers[Tag(0x0019, 0x100c)])
# get the bvec if it exists in the headers
bvec = numpy.array([0, 0, 0])
if Tag(0x0019, 0x100e) in dicom_headers:
# in case of implicit VR the private field cannot be split into an array, we do this here
bvec = numpy.array(common.get_fd_array_value(dicom_headers[Tag(0x0019, 0x100e)], 3))
# if bval is 0 or the vector is 0 no projection is needed and the vector is 0,0,0
new_bvec = numpy.array([0, 0, 0])
if bval > 0 and not (bvec == [0, 0, 0]).all():
# project the bvec and invert the y direction
new_bvec = numpy.array(
[numpy.dot(bvec, read_vector), -numpy.dot(bvec, phase_vector), numpy.dot(bvec, mosaic_vector)])
# normalize the bvec
new_bvec /= numpy.linalg.norm(new_bvec)
bvecs[index, :] = new_bvec
# save the found bvecs to the file
common.write_bvec_file(bvecs, bvec_file)
return numpy.array(bvecs)
def _create_bvecs(sorted_dicoms, bvec_file):
"""
Calculate the bvecs and write the to a bvec file
# inspired by dicom2nii from mricron
# see http://users.fmrib.ox.ac.uk/~robson/internal/Dicom2Nifti111.m
"""
if type(sorted_dicoms[0]) is list:
dicom_headers = sorted_dicoms[0][0]
else:
dicom_headers = sorted_dicoms[0]
# get the patient orientation
image_orientation = dicom_headers.ImageOrientationPatient
read_vector = numpy.array([float(image_orientation[0]), float(image_orientation[1]), float(image_orientation[2])])
phase_vector = numpy.array([float(image_orientation[3]), float(image_orientation[4]), float(image_orientation[5])])
mosaic_vector = numpy.cross(read_vector, phase_vector)
# normalize the vectors
read_vector /= numpy.linalg.norm(read_vector)
phase_vector /= numpy.linalg.norm(phase_vector)
mosaic_vector /= numpy.linalg.norm(mosaic_vector)
# create an empty array for the new bvecs
bvecs = numpy.zeros([len(sorted_dicoms), 3])
# for each slice calculate the new bvec
for index in range(0, len(sorted_dicoms)):
if type(sorted_dicoms[0]) is list:
dicom_headers = sorted_dicoms[index][0]
else:
dicom_headers = sorted_dicoms[index]
# get the bval als this is needed in some checks
bval = common.get_is_value(dicom_headers[Tag(0x0019, 0x100c)])
# get the bvec if it exists in the headers
bvec = numpy.array([0, 0, 0])
if Tag(0x0019, 0x100e) in dicom_headers:
# in case of implicit VR the private field cannot be split into an array, we do this here
bvec = numpy.array(common.get_fd_array_value(dicom_headers[Tag(0x0019, 0x100e)], 3))
# if bval is 0 or the vector is 0 no projection is needed and the vector is 0,0,0
new_bvec = numpy.array([0, 0, 0])
if bval > 0 and not (bvec == [0, 0, 0]).all():
# project the bvec and invert the y direction
new_bvec = numpy.array(
[numpy.dot(bvec, read_vector), -numpy.dot(bvec, phase_vector), numpy.dot(bvec, mosaic_vector)])
# normalize the bvec
new_bvec /= numpy.linalg.norm(new_bvec)
bvecs[index, :] = new_bvec
# save the found bvecs to the file
common.write_bvec_file(bvecs, bvec_file)
def _create_singleframe_bvals_bvecs(grouped_dicoms, bval_file, bvec_file,
nifti, nifti_file):
"""
Write the bvals from the sorted dicom files to a bval file
"""
# create the empty arrays
bvals = numpy.zeros([len(grouped_dicoms)], dtype=numpy.int32)
bvecs = numpy.zeros([len(grouped_dicoms), 3])
# loop over all timepoints and create a list with all bvals and bvecs
if _is_bval_type_a(grouped_dicoms):
bval_tag = Tag(0x2001, 0x1003)
bvec_x_tag = Tag(0x2005, 0x10b0)
bvec_y_tag = Tag(0x2005, 0x10b1)
bvec_z_tag = Tag(0x2005, 0x10b2)
for stack_index in range(0, len(grouped_dicoms)):
bvals[stack_index] = common.get_fl_value(
grouped_dicoms[stack_index][0][bval_tag])
bvecs[stack_index, :] = [
common.get_fl_value(
grouped_dicoms[stack_index][0][bvec_x_tag]),
common.get_fl_value(
grouped_dicoms[stack_index][0][bvec_y_tag]),
common.get_fl_value(grouped_dicoms[stack_index][0][bvec_z_tag])
]
elif _is_bval_type_b(grouped_dicoms):
bval_tag = Tag(0x0018, 0x9087)
bvec_tag = Tag(0x0018, 0x9089)
for stack_index in range(0, len(grouped_dicoms)):
bvals[stack_index] = common.get_fd_value(
grouped_dicoms[stack_index][0][bval_tag])
bvecs[stack_index, :] = common.get_fd_array_value(
grouped_dicoms[stack_index][0][bvec_tag], 3)
# truncate nifti if needed
nifti, bvals, bvecs = _fix_diffusion_images(bvals, bvecs, nifti,
nifti_file)
# save the found bvecs to the file
if numpy.count_nonzero(bvals) > 0 or numpy.count_nonzero(bvecs) > 0:
common.write_bval_file(bvals, bval_file)
common.write_bvec_file(bvecs, bvec_file)
else:
bval_file = None
bvec_file = None
bvals = None
bvecs = None
return nifti, bvals, bvecs, bval_file, bvec_file
def _create_singleframe_bvals_bvecs(grouped_dicoms, bval_file, bvec_file, nifti, nifti_file):
"""
Write the bvals from the sorted dicom files to a bval file
"""
# create the empty arrays
bvals = numpy.zeros([len(grouped_dicoms)], dtype=numpy.int32)
bvecs = numpy.zeros([len(grouped_dicoms), 3])
# loop over all timepoints and create a list with all bvals and bvecs
if _is_bval_type_a(grouped_dicoms):
bval_tag = Tag(0x2001, 0x1003)
bvec_x_tag = Tag(0x2005, 0x10b0)
bvec_y_tag = Tag(0x2005, 0x10b1)
bvec_z_tag = Tag(0x2005, 0x10b2)
for stack_index in range(0, len(grouped_dicoms)):
bvals[stack_index] = common.get_fl_value(grouped_dicoms[stack_index][0][bval_tag])
bvecs[stack_index, :] = [common.get_fl_value(grouped_dicoms[stack_index][0][bvec_x_tag]),
common.get_fl_value(grouped_dicoms[stack_index][0][bvec_y_tag]),
common.get_fl_value(grouped_dicoms[stack_index][0][bvec_z_tag])]
elif _is_bval_type_b(grouped_dicoms):
bval_tag = Tag(0x0018, 0x9087)
bvec_tag = Tag(0x0018, 0x9089)
for stack_index in range(0, len(grouped_dicoms)):
bvals[stack_index] = common.get_fd_value(grouped_dicoms[stack_index][0][bval_tag])
bvecs[stack_index, :] = common.get_fd_array_value(grouped_dicoms[stack_index][0][bvec_tag], 3)
# truncate nifti if needed
nifti, bvals, bvecs = _fix_diffusion_images(bvals, bvecs, nifti, nifti_file)
# save the found bvecs to the file
if numpy.count_nonzero(bvals) > 0 or numpy.count_nonzero(bvecs) > 0:
common.write_bval_file(bvals, bval_file)
common.write_bvec_file(bvecs, bvec_file)
else:
bval_file = None
bvec_file = None
bvals = None
bvecs = None
return nifti, bvals, bvecs, bval_file, bvec_file
