def update_motion_correction(self, affine_transform_sitk):
# Update rigid motion estimate
current_rigid_motion_estimate = sitkh.get_composite_sitk_affine_transform(
affine_transform_sitk, self._history_motion_corrections[-1])
self._history_motion_corrections.append(current_rigid_motion_estimate)
# New affine transform of slice after rigid motion correction
affine_transform = sitkh.get_composite_sitk_affine_transform(
affine_transform_sitk, self._affine_transform_sitk)
# Update affine transform of slice, i.e. change image origin and
# direction in physical space
self._update_affine_transform(affine_transform)
def main():
time_start = ph.start_timing()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Register an obtained reconstruction (moving) "
"to a template image/space (fixed) using rigid registration. "
"The resulting registration can optionally be applied to previously "
"obtained motion correction slice transforms so that a volumetric "
"reconstruction is possible in the (standard anatomical) space "
"defined by the fixed.", )
input_parser.add_fixed(required=True)
input_parser.add_moving(required=True)
input_parser.add_output(help="Path to registration transform (.txt)",
required=True)
input_parser.add_fixed_mask(required=False)
input_parser.add_moving_mask(required=False)
input_parser.add_option(
option_string="--initial-transform",
type=str,
help="Path to initial transform. "
"If not provided, registration will be initialized based on "
"rigid alignment of eigenbasis of the fixed/moving image masks "
"using principal component analysis",
default=None)
input_parser.add_v2v_method(
option_string="--method",
help="Registration method used for the registration.",
default="RegAladin",
)
input_parser.add_argument(
"--refine-pca",
"-refine-pca",
action='store_true',
help="If given, PCA-based initializations will be refined using "
"RegAladin registrations.")
input_parser.add_dir_input_mc()
input_parser.add_verbose(default=0)
input_parser.add_log_config(default=1)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
if not args.output.endswith(".txt"):
raise IOError("output transformation path must end in '.txt'")
dir_output = os.path.dirname(args.output)
ph.create_directory(dir_output)
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
fixed = st.Stack.from_filename(file_path=args.fixed,
file_path_mask=args.fixed_mask,
extract_slices=False)
moving = st.Stack.from_filename(file_path=args.moving,
file_path_mask=args.moving_mask,
extract_slices=False)
path_to_tmp_output = os.path.join(
DIR_TMP, ph.append_to_filename(os.path.basename(args.moving),
"_warped"))
# ---------------------------- Initialization ----------------------------
if args.initial_transform is None:
ph.print_title("Estimate initial transform using PCA")
if args.moving_mask is None or args.fixed_mask is None:
ph.print_warning("Fixed and moving masks are strongly recommended")
transform_initializer = tinit.TransformInitializer(
fixed=fixed,
moving=moving,
similarity_measure="NMI",
refine_pca_initializations=args.refine_pca,
)
transform_initializer.run()
transform_init_sitk = transform_initializer.get_transform_sitk()
else:
transform_init_sitk = sitkh.read_transform_sitk(args.initial_transform)
sitk.WriteTransform(transform_init_sitk, args.output)
# -------------------Register Reconstruction to Template-------------------
ph.print_title("Registration")
if args.method == "RegAladin":
path_to_transform_regaladin = os.path.join(DIR_TMP,
"transform_regaladin.txt")
# Convert SimpleITK to RegAladin transform
cmd = "simplereg_transform -sitk2nreg %s %s" % (
args.output, path_to_transform_regaladin)
ph.execute_command(cmd, verbose=False)
# Run NiftyReg
cmd_args = ["reg_aladin"]
cmd_args.append("-ref '%s'" % args.fixed)
cmd_args.append("-flo '%s'" % args.moving)
cmd_args.append("-res '%s'" % path_to_tmp_output)
cmd_args.append("-inaff '%s'" % path_to_transform_regaladin)
cmd_args.append("-aff '%s'" % path_to_transform_regaladin)
cmd_args.append("-rigOnly")
cmd_args.append("-ln 2") # seems to perform better for spina bifida
cmd_args.append("-voff")
if args.fixed_mask is not None:
cmd_args.append("-rmask '%s'" % args.fixed_mask)
# To avoid error "0 correspondences between blocks were found" that can
# occur for some cases. Also, disable moving mask, as this would be ignored
# anyway
cmd_args.append("-noSym")
# if args.moving_mask is not None:
# cmd_args.append("-fmask '%s'" % args.moving_mask)
ph.print_info("Run Registration (RegAladin) ... ", newline=False)
ph.execute_command(" ".join(cmd_args), verbose=False)
print("done")
# Convert RegAladin to SimpleITK transform
cmd = "simplereg_transform -nreg2sitk '%s' '%s'" % (
path_to_transform_regaladin, args.output)
ph.execute_command(cmd, verbose=False)
else:
path_to_transform_flirt = os.path.join(DIR_TMP, "transform_flirt.txt")
# Convert SimpleITK into FLIRT transform
cmd = "simplereg_transform -sitk2flirt '%s' '%s' '%s' '%s'" % (
args.output, args.fixed, args.moving, path_to_transform_flirt)
ph.execute_command(cmd, verbose=False)
# Define search angle ranges for FLIRT in all three dimensions
search_angles = [
"-searchr%s -%d %d" % (x, 180, 180) for x in ["x", "y", "z"]
]
cmd_args = ["flirt"]
cmd_args.append("-in '%s'" % args.moving)
cmd_args.append("-ref '%s'" % args.fixed)
if args.initial_transform is not None:
cmd_args.append("-init '%s'" % path_to_transform_flirt)
cmd_args.append("-omat '%s'" % path_to_transform_flirt)
cmd_args.append("-out '%s'" % path_to_tmp_output)
cmd_args.append("-dof 6")
cmd_args.append((" ").join(search_angles))
if args.moving_mask is not None:
cmd_args.append("-inweight '%s'" % args.moving_mask)
if args.fixed_mask is not None:
cmd_args.append("-refweight '%s'" % args.fixed_mask)
ph.print_info("Run Registration (FLIRT) ... ", newline=False)
ph.execute_command(" ".join(cmd_args), verbose=False)
print("done")
# Convert FLIRT to SimpleITK transform
cmd = "simplereg_transform -flirt2sitk '%s' '%s' '%s' '%s'" % (
path_to_transform_flirt, args.fixed, args.moving, args.output)
ph.execute_command(cmd, verbose=False)
if args.dir_input_mc is not None:
ph.print_title("Update Motion-Correction Transformations")
transform_sitk = sitkh.read_transform_sitk(args.output, inverse=1)
if args.dir_input_mc.endswith("/"):
subdir_mc = args.dir_input_mc.split("/")[-2]
else:
subdir_mc = args.dir_input_mc.split("/")[-1]
dir_output_mc = os.path.join(dir_output, subdir_mc)
ph.create_directory(dir_output_mc, delete_files=True)
pattern = REGEX_FILENAMES + "[.]tfm"
p = re.compile(pattern)
trafos = [t for t in os.listdir(args.dir_input_mc) if p.match(t)]
for t in trafos:
path_to_input_transform = os.path.join(args.dir_input_mc, t)
path_to_output_transform = os.path.join(dir_output_mc, t)
t_sitk = sitkh.read_transform_sitk(path_to_input_transform)
t_sitk = sitkh.get_composite_sitk_affine_transform(
transform_sitk, t_sitk)
sitk.WriteTransform(t_sitk, path_to_output_transform)
ph.print_info("%d transformations written to '%s'" %
(len(trafos), dir_output_mc))
if args.verbose:
ph.show_niftis([args.fixed, path_to_tmp_output])
elapsed_time_total = ph.stop_timing(time_start)
# Summary
ph.print_title("Summary")
print("Computational Time: %s" % (elapsed_time_total))
return 0
def run(self):
if not ph.directory_exists(self._dir_motion_correction):
raise exceptions.DirectoryNotExistent(self._dir_motion_correction)
abs_path_to_directory = os.path.abspath(self._dir_motion_correction)
for i in range(len(self._stacks)):
stack_name = self._stacks[i].get_filename()
# update stack position
path_to_stack_transform = os.path.join(abs_path_to_directory,
"%s.tfm" % stack_name)
if ph.file_exists(path_to_stack_transform):
transform_stack_sitk = sitkh.read_transform_sitk(
path_to_stack_transform)
transform_stack_sitk_inv = sitkh.read_transform_sitk(
path_to_stack_transform, inverse=True)
self._stacks[i].update_motion_correction(transform_stack_sitk)
ph.print_info("Stack '%s': Stack position updated" %
stack_name)
else:
transform_stack_sitk_inv = sitk.Euler3DTransform()
# update slice positions
pattern_trafo_slices = stack_name + self._prefix_slice + \
"([0-9]+)[.]tfm"
p = re.compile(pattern_trafo_slices)
dic_slice_transforms = {
int(p.match(f).group(1)): os.path.join(abs_path_to_directory,
p.match(f).group(0))
for f in os.listdir(abs_path_to_directory) if p.match(f)
}
slices = self._stacks[i].get_slices()
for i_slice in range(self._stacks[i].get_number_of_slices()):
if i_slice in dic_slice_transforms.keys():
transform_slice_sitk = sitkh.read_transform_sitk(
dic_slice_transforms[i_slice])
transform_slice_sitk = \
sitkh.get_composite_sitk_affine_transform(
transform_slice_sitk, transform_stack_sitk_inv)
slices[i_slice].update_motion_correction(
transform_slice_sitk)
# # ------------------------- HACK -------------------------
# # 18 Jan 2019
# # HACK to use results of a previous version where image
# # slices were still exported
# # (Bug was that after stack intensity correction, the
# # previous v2v-reg was not passed on to the final
# # registration transform):
# import niftymic.base.slice as sl
# path_to_slice = re.sub(
# ".tfm", ".nii.gz", dic_slice_transforms[i_slice])
# path_to_slice_mask = re.sub(
# ".tfm", "_mask.nii.gz", dic_slice_transforms[i_slice])
# slice_sitk = sitk.ReadImage(path_to_slice)
# slice_sitk_mask = sitk.ReadImage(path_to_slice_mask)
# hack = sl.Slice.from_sitk_image(
# # slice_sitk=slice_sitk,
# slice_sitk=slice_sitk_mask, # mask for Mask-SRR!
# slice_sitk_mask=slice_sitk_mask,
# slice_number=slices[i_slice].get_slice_number(),
# slice_thickness=slices[i_slice].get_slice_thickness(),
# )
# self._stacks[i]._slices[i_slice] = hack
# # --------------------------------------------------------
else:
self._stacks[i].delete_slice(slices[i_slice])
# print update information
ph.print_info("Stack '%s': Slice positions updated "
"(%d/%d slices deleted)" % (
stack_name,
len(self._stacks[i].get_deleted_slice_numbers()),
self._stacks[i].sitk.GetSize()[-1],
))
# delete entire stack if all slices were rejected
if self._stacks[i].get_number_of_slices() == 0:
ph.print_info("Stack '%s' removed as all slices were deleted" %
stack_name)
self._stacks[i] = None
# only return maintained stacks
self._stacks = [s for s in self._stacks if s is not None]
if len(self._stacks) == 0:
raise RuntimeError(
"All stacks removed. "
"Did you check that the correct motion-correction directory "
"was provided?")
def run(self, older_than_v3=False):
if not ph.directory_exists(self._dir_motion_correction):
raise exceptions.DirectoryNotExistent(
self._dir_motion_correction)
abs_path_to_directory = os.path.abspath(
self._dir_motion_correction)
path_to_rejected_slices = os.path.join(
abs_path_to_directory, "rejected_slices.json")
if ph.file_exists(path_to_rejected_slices):
self._rejected_slices = ph.read_dictionary_from_json(
path_to_rejected_slices)
bool_check = True
else:
self._rejected_slices = None
bool_check = False
for i in range(len(self._stacks)):
stack_name = self._stacks[i].get_filename()
if not older_than_v3:
# update stack position
path_to_stack_transform = os.path.join(
abs_path_to_directory, "%s.tfm" % stack_name)
if ph.file_exists(path_to_stack_transform):
transform_stack_sitk = sitkh.read_transform_sitk(
path_to_stack_transform)
transform_stack_sitk_inv = sitkh.read_transform_sitk(
path_to_stack_transform, inverse=True)
self._stacks[i].update_motion_correction(
transform_stack_sitk)
ph.print_info(
"Stack '%s': Stack position updated" % stack_name)
else:
transform_stack_sitk_inv = sitk.Euler3DTransform()
if self._volume_motion_only:
continue
# update slice positions
pattern_trafo_slices = stack_name + self._prefix_slice + \
"([0-9]+)[.]tfm"
p = re.compile(pattern_trafo_slices)
dic_slice_transforms = {
int(p.match(f).group(1)): os.path.join(
abs_path_to_directory, p.match(f).group(0))
for f in os.listdir(abs_path_to_directory) if p.match(f)
}
slices = self._stacks[i].get_slices()
for i_slice in range(self._stacks[i].get_number_of_slices()):
if i_slice in dic_slice_transforms.keys():
transform_slice_sitk = sitkh.read_transform_sitk(
dic_slice_transforms[i_slice])
transform_slice_sitk = \
sitkh.get_composite_sitk_affine_transform(
transform_slice_sitk, transform_stack_sitk_inv)
slices[i_slice].update_motion_correction(
transform_slice_sitk)
else:
self._stacks[i].delete_slice(slices[i_slice])
# ----------------------------- HACK -----------------------------
# 18 Jan 2019
# HACK to use results of a previous version where image slices were
# still exported.
# (There was a bug after stack intensity correction, which resulted
# in v2v-reg transforms not being part of in the final registration
# transforms; Thus, slice transformations (tfm's) were flawed and
# could not be used):
else:
# Recover suffix for mask
pattern = stack_name + self._prefix_slice + \
"[0-9]+[_]([a-zA-Z]+)[.]nii.gz"
pm = re.compile(pattern)
matches = list(set([pm.match(f).group(1) for f in os.listdir(
abs_path_to_directory) if pm.match(f)]))
if len(matches) > 1:
raise RuntimeError("Suffix mask cannot be determined")
suffix_mask = "_%s" % matches[0]
# Recover stack
path_to_stack = os.path.join(
abs_path_to_directory, "%s.nii.gz" % stack_name)
path_to_stack_mask = os.path.join(
abs_path_to_directory, "%s%s.nii.gz" % (
stack_name, suffix_mask))
stack = st.Stack.from_filename(
path_to_stack, path_to_stack_mask)
# Recover slices
pattern_trafo_slices = stack_name + self._prefix_slice + \
"([0-9]+)[.]tfm"
p = re.compile(pattern_trafo_slices)
dic_slice_transforms = {
int(p.match(f).group(1)): os.path.join(
abs_path_to_directory, p.match(f).group(0))
for f in os.listdir(abs_path_to_directory) if p.match(f)
}
slices = self._stacks[i].get_slices()
for i_slice in range(self._stacks[i].get_number_of_slices()):
if i_slice in dic_slice_transforms.keys():
path_to_slice = re.sub(
".tfm", ".nii.gz", dic_slice_transforms[i_slice])
path_to_slice_mask = re.sub(
".tfm", "%s.nii.gz" % suffix_mask,
dic_slice_transforms[i_slice])
slice_sitk = sitk.ReadImage(path_to_slice)
slice_sitk_mask = sitk.ReadImage(path_to_slice_mask)
hack = sl.Slice.from_sitk_image(
slice_sitk=slice_sitk,
# slice_sitk=slice_sitk_mask, # mask for Mask-SRR!
slice_sitk_mask=slice_sitk_mask,
slice_number=slices[i_slice].get_slice_number(),
slice_thickness=slices[
i_slice].get_slice_thickness(),
)
self._stacks[i]._slices[i_slice] = hack
else:
self._stacks[i].delete_slice(slices[i_slice])
self._stacks[i].sitk = stack.sitk
self._stacks[i].sitk_mask = stack.sitk_mask
self._stacks[i].itk = stack.itk
self._stacks[i].itk_mask = stack.itk_mask
# -----------------------------------------------------------------
# print update information
ph.print_info(
"Stack '%s': Slice positions updated "
"(%d/%d slices deleted)" % (
stack_name,
len(self._stacks[i].get_deleted_slice_numbers()),
self._stacks[i].sitk.GetSize()[-1],
)
)
# delete entire stack if all slices were rejected
if self._stacks[i].get_number_of_slices() == 0:
ph.print_info(
"Stack '%s' removed as all slices were deleted" %
stack_name)
self._stacks[i] = None
# only return maintained stacks
self._stacks = [s for s in self._stacks if s is not None]
if len(self._stacks) == 0:
raise RuntimeError(
"All stacks removed. "
"Did you check that the correct motion-correction directory "
"was provided?")
def main():
time_start = ph.start_timing()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Register an obtained reconstruction (moving) "
"to a template image/space (fixed) using rigid registration. "
"The resulting registration can optionally be applied to previously "
"obtained motion correction slice transforms so that a volumetric "
"reconstruction is possible in the (standard anatomical) space "
"defined by the fixed.", )
input_parser.add_fixed(required=True)
input_parser.add_moving(required=True)
input_parser.add_output(help="Path to registration transform (.txt)",
required=True)
input_parser.add_fixed_mask()
input_parser.add_moving_mask()
input_parser.add_dir_input_mc()
input_parser.add_search_angle(default=180)
input_parser.add_option(option_string="--initial-transform",
type=str,
help="Path to initial transform.",
default=None)
input_parser.add_option(
option_string="--test-ap-flip",
type=int,
help="Turn on/off functionality to run an additional registration "
"after an AP-flip. Seems to be more robust to find a better "
"registration outcome in general.",
default=1)
input_parser.add_option(
option_string="--use-flirt",
type=int,
help="Turn on/off functionality to use FLIRT for the registration.",
default=1)
input_parser.add_option(
option_string="--use-regaladin",
type=int,
help="Turn on/off functionality to use RegAladin for the "
"registration.",
default=1)
input_parser.add_verbose(default=0)
input_parser.add_log_config(default=1)
args = input_parser.parse_args()
input_parser.print_arguments(args)
debug = 0
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
if not args.use_regaladin and not args.use_flirt:
raise IOError("Either RegAladin or FLIRT must be activated.")
if not args.output.endswith(".txt"):
raise IOError("output transformation path must end in '.txt'")
dir_output = os.path.dirname(args.output)
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
fixed = st.Stack.from_filename(file_path=args.fixed,
file_path_mask=args.fixed_mask,
extract_slices=False)
moving = st.Stack.from_filename(file_path=args.moving,
file_path_mask=args.moving_mask,
extract_slices=False)
if args.initial_transform is not None:
transform_sitk = sitkh.read_transform_sitk(args.initial_transform)
else:
transform_sitk = sitk.AffineTransform(fixed.sitk.GetDimension())
sitk.WriteTransform(transform_sitk, args.output)
path_to_tmp_output = os.path.join(
DIR_TMP, ph.append_to_filename(os.path.basename(args.moving),
"_warped"))
# -------------------Register Reconstruction to Template-------------------
ph.print_title("Register Reconstruction to Template")
if args.use_flirt:
path_to_transform_flirt = os.path.join(DIR_TMP, "transform_flirt.txt")
# Convert SimpleITK into FLIRT transform
cmd = "simplereg_transform -sitk2flirt %s %s %s %s" % (
args.output, args.fixed, args.moving, path_to_transform_flirt)
ph.execute_command(cmd, verbose=False)
# Define search angle ranges for FLIRT in all three dimensions
search_angles = [
"-searchr%s -%d %d" % (x, args.search_angle, args.search_angle)
for x in ["x", "y", "z"]
]
# flt = nipype.interfaces.fsl.FLIRT()
# flt.inputs.in_file = args.moving
# flt.inputs.reference = args.fixed
# if args.initial_transform is not None:
# flt.inputs.in_matrix_file = path_to_transform_flirt
# flt.inputs.out_matrix_file = path_to_transform_flirt
# # flt.inputs.output_type = "NIFTI_GZ"
# flt.inputs.out_file = path_to_tmp_output
# flt.inputs.args = "-dof 6"
# flt.inputs.args += " %s" % " ".join(search_angles)
# if args.moving_mask is not None:
# flt.inputs.in_weight = args.moving_mask
# if args.fixed_mask is not None:
# flt.inputs.ref_weight = args.fixed_mask
# ph.print_info("Run Registration (FLIRT) ... ", newline=False)
# flt.run()
# print("done")
cmd_args = ["flirt"]
cmd_args.append("-in %s" % args.moving)
cmd_args.append("-ref %s" % args.fixed)
if args.initial_transform is not None:
cmd_args.append("-init %s" % path_to_transform_flirt)
cmd_args.append("-omat %s" % path_to_transform_flirt)
cmd_args.append("-out %s" % path_to_tmp_output)
cmd_args.append("-dof 6")
cmd_args.append((" ").join(search_angles))
if args.moving_mask is not None:
cmd_args.append("-inweight %s" % args.moving_mask)
if args.fixed_mask is not None:
cmd_args.append("-refweight %s" % args.fixed_mask)
ph.print_info("Run Registration (FLIRT) ... ", newline=False)
ph.execute_command(" ".join(cmd_args), verbose=False)
print("done")
# Convert FLIRT to SimpleITK transform
cmd = "simplereg_transform -flirt2sitk %s %s %s %s" % (
path_to_transform_flirt, args.fixed, args.moving, args.output)
ph.execute_command(cmd, verbose=False)
if debug:
ph.show_niftis([args.fixed, path_to_tmp_output])
# Additionally, use RegAladin for more accurate alignment
# Rationale: FLIRT has better capture range, but RegAladin seems to
# find better alignment once it is within its capture range.
if args.use_regaladin:
path_to_transform_regaladin = os.path.join(DIR_TMP,
"transform_regaladin.txt")
# Convert SimpleITK to RegAladin transform
cmd = "simplereg_transform -sitk2nreg %s %s" % (
args.output, path_to_transform_regaladin)
ph.execute_command(cmd, verbose=False)
# nreg = nipype.interfaces.niftyreg.RegAladin()
# nreg.inputs.ref_file = args.fixed
# nreg.inputs.flo_file = args.moving
# nreg.inputs.res_file = path_to_tmp_output
# nreg.inputs.in_aff_file = path_to_transform_regaladin
# nreg.inputs.aff_file = path_to_transform_regaladin
# nreg.inputs.args = "-rigOnly -voff"
# if args.moving_mask is not None:
# nreg.inputs.fmask_file = args.moving_mask
# if args.fixed_mask is not None:
# nreg.inputs.rmask_file = args.fixed_mask
# ph.print_info("Run Registration (RegAladin) ... ", newline=False)
# nreg.run()
# print("done")
cmd_args = ["reg_aladin"]
cmd_args.append("-ref %s" % args.fixed)
cmd_args.append("-flo %s" % args.moving)
cmd_args.append("-res %s" % path_to_tmp_output)
if args.initial_transform is not None or args.use_flirt == 1:
cmd_args.append("-inaff %s" % path_to_transform_regaladin)
cmd_args.append("-aff %s" % path_to_transform_regaladin)
# cmd_args.append("-cog")
# cmd_args.append("-ln 2")
cmd_args.append("-rigOnly")
cmd_args.append("-voff")
if args.moving_mask is not None:
cmd_args.append("-fmask %s" % args.moving_mask)
if args.fixed_mask is not None:
cmd_args.append("-rmask %s" % args.fixed_mask)
ph.print_info("Run Registration (RegAladin) ... ", newline=False)
ph.execute_command(" ".join(cmd_args), verbose=False)
print("done")
# Convert RegAladin to SimpleITK transform
cmd = "simplereg_transform -nreg2sitk %s %s" % (
path_to_transform_regaladin, args.output)
ph.execute_command(cmd, verbose=False)
if debug:
ph.show_niftis([args.fixed, path_to_tmp_output])
if args.test_ap_flip:
path_to_transform_flip = os.path.join(DIR_TMP, "transform_flip.txt")
path_to_tmp_output_flip = os.path.join(DIR_TMP, "output_flip.nii.gz")
# Get AP-flip transform
transform_ap_flip_sitk = get_ap_flip_transform(args.fixed)
path_to_transform_flip_regaladin = os.path.join(
DIR_TMP, "transform_flip_regaladin.txt")
sitk.WriteTransform(transform_ap_flip_sitk, path_to_transform_flip)
# Compose current transform with AP flip transform
cmd = "simplereg_transform -c %s %s %s" % (
args.output, path_to_transform_flip, path_to_transform_flip)
ph.execute_command(cmd, verbose=False)
# Convert SimpleITK to RegAladin transform
cmd = "simplereg_transform -sitk2nreg %s %s" % (
path_to_transform_flip, path_to_transform_flip_regaladin)
ph.execute_command(cmd, verbose=False)
# nreg = nipype.interfaces.niftyreg.RegAladin()
# nreg.inputs.ref_file = args.fixed
# nreg.inputs.flo_file = args.moving
# nreg.inputs.res_file = path_to_tmp_output_flip
# nreg.inputs.in_aff_file = path_to_transform_flip_regaladin
# nreg.inputs.aff_file = path_to_transform_flip_regaladin
# nreg.inputs.args = "-rigOnly -voff"
# if args.moving_mask is not None:
# nreg.inputs.fmask_file = args.moving_mask
# if args.fixed_mask is not None:
# nreg.inputs.rmask_file = args.fixed_mask
# ph.print_info("Run Registration AP-flipped (RegAladin) ... ",
# newline=False)
# nreg.run()
# print("done")
cmd_args = ["reg_aladin"]
cmd_args.append("-ref %s" % args.fixed)
cmd_args.append("-flo %s" % args.moving)
cmd_args.append("-res %s" % path_to_tmp_output_flip)
cmd_args.append("-inaff %s" % path_to_transform_flip_regaladin)
cmd_args.append("-aff %s" % path_to_transform_flip_regaladin)
cmd_args.append("-rigOnly")
# cmd_args.append("-ln 2")
cmd_args.append("-voff")
if args.moving_mask is not None:
cmd_args.append("-fmask %s" % args.moving_mask)
if args.fixed_mask is not None:
cmd_args.append("-rmask %s" % args.fixed_mask)
ph.print_info("Run Registration AP-flipped (RegAladin) ... ",
newline=False)
ph.execute_command(" ".join(cmd_args), verbose=False)
print("done")
if debug:
ph.show_niftis(
[args.fixed, path_to_tmp_output, path_to_tmp_output_flip])
warped_moving = st.Stack.from_filename(path_to_tmp_output,
extract_slices=False)
warped_moving_flip = st.Stack.from_filename(path_to_tmp_output_flip,
extract_slices=False)
fixed = st.Stack.from_filename(args.fixed, args.fixed_mask)
stacks = [warped_moving, warped_moving_flip]
image_similarity_evaluator = ise.ImageSimilarityEvaluator(
stacks=stacks, reference=fixed)
image_similarity_evaluator.compute_similarities()
similarities = image_similarity_evaluator.get_similarities()
if similarities["NMI"][1] > similarities["NMI"][0]:
ph.print_info("AP-flipped outcome better")
# Convert RegAladin to SimpleITK transform
cmd = "simplereg_transform -nreg2sitk %s %s" % (
path_to_transform_flip_regaladin, args.output)
ph.execute_command(cmd, verbose=False)
# Copy better outcome
cmd = "cp -p %s %s" % (path_to_tmp_output_flip, path_to_tmp_output)
ph.execute_command(cmd, verbose=False)
else:
ph.print_info("AP-flip does not improve outcome")
if args.dir_input_mc is not None:
transform_sitk = sitkh.read_transform_sitk(args.output, inverse=1)
if args.dir_input_mc.endswith("/"):
subdir_mc = args.dir_input_mc.split("/")[-2]
else:
subdir_mc = args.dir_input_mc.split("/")[-1]
dir_output_mc = os.path.join(dir_output, subdir_mc)
ph.create_directory(dir_output_mc, delete_files=True)
pattern = REGEX_FILENAMES + "[.]tfm"
p = re.compile(pattern)
trafos = [t for t in os.listdir(args.dir_input_mc) if p.match(t)]
for t in trafos:
path_to_input_transform = os.path.join(args.dir_input_mc, t)
path_to_output_transform = os.path.join(dir_output_mc, t)
t_sitk = sitkh.read_transform_sitk(path_to_input_transform)
t_sitk = sitkh.get_composite_sitk_affine_transform(
transform_sitk, t_sitk)
sitk.WriteTransform(t_sitk, path_to_output_transform)
if args.verbose:
ph.show_niftis([args.fixed, path_to_tmp_output])
elapsed_time_total = ph.stop_timing(time_start)
# Summary
ph.print_title("Summary")
print("Computational Time: %s" % (elapsed_time_total))
return 0
def get_inplane_corrupted_stack(stack,
angle_z,
center_2D,
translation_2D,
scale=1,
intensity_scale=1,
intensity_bias=0,
debug=0,
random=False):
# Convert to 3D:
translation_3D = np.zeros(3)
translation_3D[0:-1] = translation_2D
center_3D = np.zeros(3)
center_3D[0:-1] = center_2D
# Transform to align physical coordinate system with stack-coordinate
# system
affine_centering_sitk = sitk.AffineTransform(3)
affine_centering_sitk.SetMatrix(stack.sitk.GetDirection())
affine_centering_sitk.SetTranslation(stack.sitk.GetOrigin())
# Corrupt first stack towards positive direction
if random:
angle_z_1 = -angle_z * np.random.rand(1)[0]
else:
angle_z_1 = -angle_z
in_plane_motion_sitk = sitk.Euler3DTransform()
in_plane_motion_sitk.SetRotation(0, 0, angle_z_1)
in_plane_motion_sitk.SetCenter(center_3D)
in_plane_motion_sitk.SetTranslation(translation_3D)
motion_sitk = sitkh.get_composite_sitk_affine_transform(
in_plane_motion_sitk,
sitk.AffineTransform(affine_centering_sitk.GetInverse()))
motion_sitk = sitkh.get_composite_sitk_affine_transform(
affine_centering_sitk, motion_sitk)
stack_corrupted_resampled_sitk = sitk.Resample(stack.sitk, motion_sitk,
sitk.sitkLinear)
stack_corrupted_resampled_sitk_mask = sitk.Resample(
stack.sitk_mask, motion_sitk, sitk.sitkLinear)
# Corrupt first stack towards negative direction
if random:
angle_z_2 = -angle_z * np.random.rand(1)[0]
else:
angle_z_2 = -angle_z
in_plane_motion_2_sitk = sitk.Euler3DTransform()
in_plane_motion_2_sitk.SetRotation(0, 0, angle_z_2)
in_plane_motion_2_sitk.SetCenter(center_3D)
in_plane_motion_2_sitk.SetTranslation(-translation_3D)
motion_2_sitk = sitkh.get_composite_sitk_affine_transform(
in_plane_motion_2_sitk,
sitk.AffineTransform(affine_centering_sitk.GetInverse()))
motion_2_sitk = sitkh.get_composite_sitk_affine_transform(
affine_centering_sitk, motion_2_sitk)
stack_corrupted_2_resampled_sitk = sitk.Resample(stack.sitk, motion_2_sitk,
sitk.sitkLinear)
stack_corrupted_2_resampled_sitk_mask = sitk.Resample(
stack.sitk_mask, motion_2_sitk, sitk.sitkLinear)
# Create stack based on those two corrupted stacks
nda = sitk.GetArrayFromImage(stack_corrupted_resampled_sitk)
nda_mask = sitk.GetArrayFromImage(stack_corrupted_resampled_sitk_mask)
nda_neg = sitk.GetArrayFromImage(stack_corrupted_2_resampled_sitk)
nda_neg_mask = sitk.GetArrayFromImage(
stack_corrupted_2_resampled_sitk_mask)
for i in range(0, stack.sitk.GetDepth(), 2):
nda[i, :, :] = nda_neg[i, :, :]
nda_mask[i, :, :] = nda_neg_mask[i, :, :]
stack_corrupted_sitk = sitk.GetImageFromArray(
(nda - intensity_bias) / intensity_scale)
stack_corrupted_sitk_mask = sitk.GetImageFromArray(nda_mask)
stack_corrupted_sitk.CopyInformation(stack.sitk)
stack_corrupted_sitk_mask.CopyInformation(stack.sitk_mask)
# Debug: Show corrupted stacks (before scaling)
if debug:
sitkh.show_sitk_image([
stack.sitk, stack_corrupted_resampled_sitk,
stack_corrupted_2_resampled_sitk, stack_corrupted_sitk
],
title=[
"original", "corrupted_1", "corrupted_2",
"corrupted_final_from_1_and_2"
])
# Update in-plane scaling
spacing = np.array(stack.sitk.GetSpacing())
spacing[0:-1] /= scale
stack_corrupted_sitk.SetSpacing(spacing)
stack_corrupted_sitk_mask.SetSpacing(spacing)
# Create Stack object
stack_corrupted = st.Stack.from_sitk_image(stack_corrupted_sitk,
"stack_corrupted",
stack_corrupted_sitk_mask)
# Debug: Show corrupted stacks (after scaling)
if debug:
stack_corrupted_resampled_sitk = sitk.Resample(stack_corrupted.sitk,
stack.sitk)
sitkh.show_sitk_image([stack.sitk, stack_corrupted_resampled_sitk],
title=["original", "corrupted"])
return stack_corrupted, motion_sitk, motion_2_sitk
def main():
time_start = ph.start_timing()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Register an obtained reconstruction (moving) "
"to a template image/space (fixed) using rigid registration. "
"The resulting registration can optionally be applied to previously "
"obtained motion correction slice transforms so that a volumetric "
"reconstruction is possible in the (standard anatomical) space "
"defined by the fixed.", )
input_parser.add_fixed(required=True)
input_parser.add_moving(
required=True,
nargs="+",
help="Specify moving image to be warped to fixed space. "
"If multiple images are provided, all images will be transformed "
"uniformly according to the registration obtained for the first one.")
input_parser.add_dir_output(required=True)
input_parser.add_dir_input()
input_parser.add_suffix_mask(default="_mask")
input_parser.add_search_angle(default=180)
input_parser.add_option(
option_string="--transform-only",
type=int,
help="Turn on/off functionality to transform moving image(s) to fixed "
"image only, i.e. no resampling to fixed image space",
default=0)
input_parser.add_option(
option_string="--write-transform",
type=int,
help="Turn on/off functionality to write registration transform",
default=0)
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
use_reg_aladin_for_refinement = True
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
data_reader = dr.MultipleImagesReader(args.moving, suffix_mask="_mask")
data_reader.read_data()
moving = data_reader.get_data()
data_reader = dr.MultipleImagesReader([args.fixed], suffix_mask="_mask")
data_reader.read_data()
fixed = data_reader.get_data()[0]
# -------------------Register Reconstruction to Template-------------------
ph.print_title("Register Reconstruction to Template")
# Define search angle ranges for FLIRT in all three dimensions
search_angles = [
"-searchr%s -%d %d" % (x, args.search_angle, args.search_angle)
for x in ["x", "y", "z"]
]
search_angles = (" ").join(search_angles)
options_args = []
options_args.append(search_angles)
# cost = "mutualinfo"
# options_args.append("-searchcost %s -cost %s" % (cost, cost))
registration = regflirt.FLIRT(
fixed=moving[0],
moving=fixed,
# use_fixed_mask=True,
# use_moving_mask=True, # moving mask only seems to work for SB cases
registration_type="Rigid",
use_verbose=False,
options=(" ").join(options_args),
)
ph.print_info("Run Registration (FLIRT) ... ", newline=False)
registration.run()
print("done")
transform_sitk = registration.get_registration_transform_sitk()
if args.write_transform:
path_to_transform = os.path.join(args.dir_output,
"registration_transform_sitk.txt")
sitk.WriteTransform(transform_sitk, path_to_transform)
# Apply rigidly transform to align reconstruction (moving) with template
# (fixed)
for m in moving:
m.update_motion_correction(transform_sitk)
# Additionally, use RegAladin for more accurate alignment
# Rationale: FLIRT has better capture range, but RegAladin seems to
# find better alignment once it is within its capture range.
if use_reg_aladin_for_refinement:
registration = niftyreg.RegAladin(
fixed=m,
use_fixed_mask=True,
moving=fixed,
registration_type="Rigid",
use_verbose=False,
)
ph.print_info("Run Registration (RegAladin) ... ", newline=False)
registration.run()
print("done")
transform2_sitk = registration.get_registration_transform_sitk()
m.update_motion_correction(transform2_sitk)
transform_sitk = sitkh.get_composite_sitk_affine_transform(
transform2_sitk, transform_sitk)
if args.transform_only:
for m in moving:
m.write(args.dir_output, write_mask=False)
ph.exit()
# Resample reconstruction (moving) to template space (fixed)
warped_moving = [
m.get_resampled_stack(fixed.sitk, interpolator="Linear")
for m in moving
]
for wm in warped_moving:
wm.set_filename(wm.get_filename() + "ResamplingToTemplateSpace")
if args.verbose:
sitkh.show_stacks([fixed, wm], segmentation=fixed)
# Write resampled reconstruction (moving)
wm.write(args.dir_output, write_mask=False)
if args.dir_input is not None:
data_reader = dr.ImageSlicesDirectoryReader(
path_to_directory=args.dir_input, suffix_mask=args.suffix_mask)
data_reader.read_data()
stacks = data_reader.get_data()
for i, stack in enumerate(stacks):
stack.update_motion_correction(transform_sitk)
ph.print_info("Stack %d/%d: All slice transforms updated" %
(i + 1, len(stacks)))
# Write transformed slices
stack.write(
os.path.join(args.dir_output, "motion_correction"),
write_mask=True,
write_slices=True,
write_transforms=True,
suffix_mask=args.suffix_mask,
)
elapsed_time_total = ph.stop_timing(time_start)
# Summary
ph.print_title("Summary")
print("Computational Time: %s" % (elapsed_time_total))
return 0
