def main():
input_parser = InputArgparser(
description="Show data/slice coverage over specified reconstruction "
"space.", )
input_parser.add_filenames(required=True)
input_parser.add_reconstruction_space(required=True)
input_parser.add_output(required=True)
input_parser.add_dir_input_mc()
input_parser.add_slice_thicknesses()
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
data_reader = dr.MultipleImagesReader(
file_paths=args.filenames,
dir_motion_correction=args.dir_input_mc,
stacks_slice_thicknesses=args.slice_thicknesses,
)
data_reader.read_data()
stacks = data_reader.get_data()
reconstruction_space_sitk = sitk.ReadImage(args.reconstruction_space)
slice_coverage = sc.SliceCoverage(
stacks=stacks,
reconstruction_sitk=reconstruction_space_sitk,
)
slice_coverage.run()
coverage_sitk = slice_coverage.get_coverage_sitk()
dw.DataWriter.write_mask(coverage_sitk, args.output)
if args.verbose:
niftis = [
args.reconstruction_space,
args.output,
]
ph.show_niftis(niftis)
def main():
time_start = ph.start_timing()
# Set print options for numpy
np.set_printoptions(precision=3)
# Read input
input_parser = InputArgparser(
description="Script to study reconstruction parameters and their "
"impact on the volumetric reconstruction quality. "
"This script can only be used to sweep through one single parameter, "
"e.g. the regularization parameter 'alpha'. ")
input_parser.add_filenames(required=True)
input_parser.add_filenames_masks()
input_parser.add_suffix_mask(default="_mask")
input_parser.add_dir_input_mc()
input_parser.add_dir_output(required=True)
input_parser.add_reconstruction_space()
input_parser.add_reference(
help="Path to reference NIfTI image file. If given the volumetric "
"reconstructed is performed in this physical space. "
"Either a reconstruction space or a reference must be provided",
required=False)
input_parser.add_reference_mask(default=None)
input_parser.add_study_name()
input_parser.add_reconstruction_type(default="TK1L2")
input_parser.add_measures(
default=["PSNR", "MAE", "RMSE", "SSIM", "NCC", "NMI"])
input_parser.add_tv_solver(default="PD")
input_parser.add_iterations(default=50)
input_parser.add_rho(default=0.1)
input_parser.add_iter_max(default=10)
input_parser.add_minimizer(default="lsmr")
input_parser.add_log_config(default=1)
input_parser.add_use_masks_srr(default=0)
input_parser.add_verbose(default=1)
input_parser.add_slice_thicknesses(default=None)
input_parser.add_argument(
"--append",
"-append",
action='store_true',
help="If given, results are appended to previously executed parameter "
"study with identical parameters and study name store in the output "
"directory.")
# Range for parameter sweeps
input_parser.add_alphas(default=list(np.linspace(0.01, 0.5, 5)))
input_parser.add_data_losses(default=["linear"]
# default=["linear", "arctan"]
)
input_parser.add_data_loss_scales(default=[1]
# default=[0.1, 0.5, 1.5]
)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if args.reference is None and args.reconstruction_space is None:
raise IOError("Either reference (--reference) or reconstruction space "
"(--reconstruction-space) must be provided.")
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
data_reader = dr.MultipleImagesReader(
file_paths=args.filenames,
file_paths_masks=args.filenames_masks,
suffix_mask=args.suffix_mask,
dir_motion_correction=args.dir_input_mc,
stacks_slice_thicknesses=args.slice_thicknesses,
)
data_reader.read_data()
stacks = data_reader.get_data()
ph.print_info("%d input stacks read for further processing" % len(stacks))
if args.reference is not None:
reference = st.Stack.from_filename(file_path=args.reference,
file_path_mask=args.reference_mask,
extract_slices=False)
reconstruction_space = stacks[0].get_resampled_stack(reference.sitk)
reconstruction_space = \
reconstruction_space.get_stack_multiplied_with_mask()
x_ref = sitk.GetArrayFromImage(reference.sitk).flatten()
x_ref_mask = sitk.GetArrayFromImage(reference.sitk_mask).flatten()
else:
reconstruction_space = st.Stack.from_filename(
file_path=args.reconstruction_space, extract_slices=False)
reconstruction_space = stacks[0].get_resampled_stack(
reconstruction_space.sitk)
reconstruction_space = \
reconstruction_space.get_stack_multiplied_with_mask()
x_ref = None
x_ref_mask = None
# ----------------------------Set Up Parameters----------------------------
parameters = {}
parameters["alpha"] = args.alphas
if len(args.data_losses) > 1:
parameters["data_loss"] = args.data_losses
if len(args.data_loss_scales) > 1:
parameters["data_loss_scale"] = args.data_loss_scales
# --------------------------Set Up Parameter Study-------------------------
ph.print_title("Run Parameter Study")
if args.study_name is None:
name = args.reconstruction_type
else:
name = args.study_name
reconstruction_info = {
"shape": reconstruction_space.sitk.GetSize()[::-1],
"origin": reconstruction_space.sitk.GetOrigin(),
"spacing": reconstruction_space.sitk.GetSpacing(),
"direction": reconstruction_space.sitk.GetDirection(),
}
# Create Tikhonov solver from which all information can be extracted
# (also for other reconstruction types)
tmp = tk.TikhonovSolver(
stacks=stacks,
reconstruction=reconstruction_space,
alpha=args.alphas[0],
iter_max=args.iter_max,
data_loss=args.data_losses[0],
data_loss_scale=args.data_loss_scales[0],
reg_type="TK1",
minimizer=args.minimizer,
verbose=args.verbose,
use_masks=args.use_masks_srr,
)
solver = tmp.get_solver()
parameter_study_interface = \
deconv_interface.DeconvolutionParameterStudyInterface(
A=solver.get_A(),
A_adj=solver.get_A_adj(),
D=solver.get_B(),
D_adj=solver.get_B_adj(),
b=solver.get_b(),
x0=solver.get_x0(),
alpha=solver.get_alpha(),
x_scale=solver.get_x_scale(),
data_loss=solver.get_data_loss(),
data_loss_scale=solver.get_data_loss_scale(),
iter_max=solver.get_iter_max(),
minimizer=solver.get_minimizer(),
iterations=args.iterations,
measures=args.measures,
dimension=3,
L2=16. / reconstruction_space.sitk.GetSpacing()[0]**2,
reconstruction_type=args.reconstruction_type,
rho=args.rho,
dir_output=args.dir_output,
parameters=parameters,
name=name,
reconstruction_info=reconstruction_info,
x_ref=x_ref,
x_ref_mask=x_ref_mask,
tv_solver=args.tv_solver,
verbose=args.verbose,
append=args.append,
)
parameter_study_interface.set_up_parameter_study()
parameter_study = parameter_study_interface.get_parameter_study()
# Run parameter study
parameter_study.run()
print("\nComputational time for Deconvolution Parameter Study %s: %s" %
(name, parameter_study.get_computational_time()))
return 0
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 main():
input_parser = InputArgparser(
description="Simulate stacks from obtained reconstruction. "
"Script simulates/projects the slices at estimated positions "
"within reconstructed volume. Ideally, if motion correction was "
"correct, the resulting stack of such obtained projected slices, "
"corresponds to the originally acquired (motion corrupted) data.",
)
input_parser.add_filenames(required=True)
input_parser.add_filenames_masks()
input_parser.add_dir_input_mc(required=True)
input_parser.add_reconstruction(required=True)
input_parser.add_dir_output(required=True)
input_parser.add_suffix_mask(default="_mask")
input_parser.add_prefix_output(default="Simulated_")
input_parser.add_option(
option_string="--copy-data",
type=int,
help="Turn on/off copying of original data (including masks) to "
"output folder.",
default=0)
input_parser.add_option(
option_string="--reconstruction-mask",
type=str,
help="If given, reconstruction image mask is propagated to "
"simulated stack(s) of slices as well",
default=None)
input_parser.add_interpolator(
option_string="--interpolator-mask",
help="Choose the interpolator type to propagate the reconstruction "
"mask (%s)." % (INTERPOLATOR_TYPES),
default="NearestNeighbor")
input_parser.add_log_config(default=0)
input_parser.add_verbose(default=0)
input_parser.add_slice_thicknesses(default=None)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if args.interpolator_mask not in ALLOWED_INTERPOLATORS:
raise IOError(
"Unknown interpolator provided. Possible choices are %s" % (
INTERPOLATOR_TYPES))
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
# Read motion corrected data
data_reader = dr.MultipleImagesReader(
file_paths=args.filenames,
file_paths_masks=args.filenames_masks,
suffix_mask=args.suffix_mask,
dir_motion_correction=args.dir_input_mc,
stacks_slice_thicknesses=args.slice_thicknesses,
)
data_reader.read_data()
stacks = data_reader.get_data()
reconstruction = st.Stack.from_filename(
args.reconstruction, args.reconstruction_mask, extract_slices=False)
linear_operators = lin_op.LinearOperators()
for i, stack in enumerate(stacks):
# initialize image data array(s)
nda = np.zeros_like(sitk.GetArrayFromImage(stack.sitk))
nda[:] = np.nan
if args.reconstruction_mask:
nda_mask = np.zeros_like(sitk.GetArrayFromImage(stack.sitk_mask))
slices = stack.get_slices()
kept_indices = [s.get_slice_number() for s in slices]
# Fill stack information "as if slice was acquired consecutively"
# Therefore, simulated stack slices correspond to acquired slices
# (in case motion correction was correct)
for j in range(nda.shape[0]):
if j in kept_indices:
index = kept_indices.index(j)
simulated_slice = linear_operators.A(
reconstruction,
slices[index],
interpolator_mask=args.interpolator_mask
)
nda[j, :, :] = sitk.GetArrayFromImage(simulated_slice.sitk)
if args.reconstruction_mask:
nda_mask[j, :, :] = sitk.GetArrayFromImage(
simulated_slice.sitk_mask)
# Create nifti image with same image header as original stack
simulated_stack_sitk = sitk.GetImageFromArray(nda)
simulated_stack_sitk.CopyInformation(stack.sitk)
if args.reconstruction_mask:
simulated_stack_sitk_mask = sitk.GetImageFromArray(nda_mask)
simulated_stack_sitk_mask.CopyInformation(stack.sitk_mask)
else:
simulated_stack_sitk_mask = None
simulated_stack = st.Stack.from_sitk_image(
image_sitk=simulated_stack_sitk,
image_sitk_mask=simulated_stack_sitk_mask,
filename=args.prefix_output + stack.get_filename(),
extract_slices=False,
slice_thickness=stack.get_slice_thickness(),
)
if args.verbose:
sitkh.show_stacks([
stack, simulated_stack],
segmentation=stack)
simulated_stack.write(
args.dir_output,
write_mask=False,
write_slices=False,
suffix_mask=args.suffix_mask)
if args.copy_data:
stack.write(
args.dir_output,
write_mask=True,
write_slices=False,
suffix_mask="_mask")
return 0
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 main():
time_start = ph.start_timing()
# Set print options for numpy
np.set_printoptions(precision=3)
# Read input
input_parser = InputArgparser(
description="Volumetric MRI reconstruction framework to reconstruct "
"an isotropic, high-resolution 3D volume from multiple "
"motion-corrected (or static) stacks of low-resolution slices.", )
input_parser.add_filenames(required=True)
input_parser.add_filenames_masks()
input_parser.add_dir_input_mc()
input_parser.add_output(required=True)
input_parser.add_suffix_mask(default="_mask")
input_parser.add_target_stack(default=None)
input_parser.add_extra_frame_target(default=10)
input_parser.add_isotropic_resolution(default=None)
input_parser.add_intensity_correction(default=1)
input_parser.add_reconstruction_space(default=None)
input_parser.add_minimizer(default="lsmr")
input_parser.add_iter_max(default=10)
input_parser.add_reconstruction_type(default="TK1L2")
input_parser.add_data_loss(default="linear")
input_parser.add_data_loss_scale(default=1)
input_parser.add_alpha(default=0.01 # TK1L2
# default=0.006 #TVL2, HuberL2
)
input_parser.add_rho(default=0.5)
input_parser.add_tv_solver(default="PD")
input_parser.add_pd_alg_type(default="ALG2")
input_parser.add_iterations(default=15)
input_parser.add_log_config(default=1)
input_parser.add_use_masks_srr(default=0)
input_parser.add_slice_thicknesses(default=None)
input_parser.add_verbose(default=0)
input_parser.add_viewer(default="itksnap")
input_parser.add_argument(
"--mask",
"-mask",
action='store_true',
help="If given, input images are interpreted as image masks. "
"Obtained volumetric reconstruction will be exported in uint8 format.")
input_parser.add_argument(
"--sda",
"-sda",
action='store_true',
help="If given, the volume is reconstructed using "
"Scattered Data Approximation (Vercauteren et al., 2006). "
"--alpha is considered the value for the standard deviation then. "
"Recommended value is, e.g., --alpha 0.8")
args = input_parser.parse_args()
input_parser.print_arguments(args)
if args.reconstruction_type not in ["TK1L2", "TVL2", "HuberL2"]:
raise IOError("Reconstruction type unknown")
if np.alltrue([not args.output.endswith(t) for t in ALLOWED_EXTENSIONS]):
raise ValueError("output filename '%s' invalid; "
"allowed image extensions are: %s" %
(args.output, ", ".join(ALLOWED_EXTENSIONS)))
dir_output = os.path.dirname(args.output)
ph.create_directory(dir_output)
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
if args.verbose:
show_niftis = []
# show_niftis = [f for f in args.filenames]
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
if args.mask:
filenames_masks = args.filenames
else:
filenames_masks = args.filenames_masks
data_reader = dr.MultipleImagesReader(
file_paths=args.filenames,
file_paths_masks=filenames_masks,
suffix_mask=args.suffix_mask,
dir_motion_correction=args.dir_input_mc,
stacks_slice_thicknesses=args.slice_thicknesses,
)
data_reader.read_data()
stacks = data_reader.get_data()
ph.print_info("%d input stacks read for further processing" % len(stacks))
# Specify target stack for intensity correction and reconstruction space
if args.target_stack is None:
target_stack_index = 0
else:
filenames = ["%s.nii.gz" % s.get_filename() for s in stacks]
filename_target_stack = os.path.basename(args.target_stack)
try:
target_stack_index = filenames.index(filename_target_stack)
except ValueError as e:
raise ValueError(
"--target-stack must correspond to an image as provided by "
"--filenames")
# ---------------------------Intensity Correction--------------------------
if args.intensity_correction and not args.mask:
ph.print_title("Intensity Correction")
intensity_corrector = ic.IntensityCorrection()
intensity_corrector.use_individual_slice_correction(False)
intensity_corrector.use_stack_mask(True)
intensity_corrector.use_reference_mask(True)
intensity_corrector.use_verbose(False)
for i, stack in enumerate(stacks):
if i == target_stack_index:
ph.print_info("Stack %d (%s): Reference image. Skipped." %
(i + 1, stack.get_filename()))
continue
else:
ph.print_info("Stack %d (%s): Intensity Correction ... " %
(i + 1, stack.get_filename()),
newline=False)
intensity_corrector.set_stack(stack)
intensity_corrector.set_reference(
stacks[target_stack_index].get_resampled_stack(
resampling_grid=stack.sitk,
interpolator="NearestNeighbor",
))
intensity_corrector.run_linear_intensity_correction()
stacks[i] = intensity_corrector.get_intensity_corrected_stack()
print("done (c1 = %g) " %
intensity_corrector.get_intensity_correction_coefficients())
# -------------------------Volumetric Reconstruction-----------------------
ph.print_title("Volumetric Reconstruction")
# Reconstruction space is given isotropically resampled target stack
if args.reconstruction_space is None:
recon0 = stacks[target_stack_index].get_isotropically_resampled_stack(
resolution=args.isotropic_resolution,
extra_frame=args.extra_frame_target)
recon0 = recon0.get_cropped_stack_based_on_mask(
boundary_i=args.extra_frame_target,
boundary_j=args.extra_frame_target,
boundary_k=args.extra_frame_target,
unit="mm",
)
# Reconstruction space was provided by user
else:
recon0 = st.Stack.from_filename(args.reconstruction_space,
extract_slices=False)
# Change resolution for isotropic resolution if provided by user
if args.isotropic_resolution is not None:
recon0 = recon0.get_isotropically_resampled_stack(
args.isotropic_resolution)
# Use image information of selected target stack as recon0 serves
# as initial value for reconstruction
recon0 = stacks[target_stack_index].get_resampled_stack(recon0.sitk)
recon0 = recon0.get_stack_multiplied_with_mask()
ph.print_info("Reconstruction space defined with %s mm3 resolution" %
" x ".join(["%.2f" % s for s in recon0.sitk.GetSpacing()]))
if args.sda:
ph.print_title("Compute SDA reconstruction")
SDA = sda.ScatteredDataApproximation(stacks,
recon0,
sigma=args.alpha,
sda_mask=args.mask)
SDA.run()
recon = SDA.get_reconstruction()
if args.mask:
dw.DataWriter.write_mask(recon.sitk_mask, args.output)
else:
dw.DataWriter.write_image(recon.sitk, args.output)
if args.verbose:
show_niftis.insert(0, args.output)
else:
if args.reconstruction_type in ["TVL2", "HuberL2"]:
ph.print_title("Compute Initial value for %s" %
args.reconstruction_type)
SRR0 = tk.TikhonovSolver(
stacks=stacks,
reconstruction=recon0,
alpha=args.alpha,
iter_max=np.min([5, args.iter_max]),
reg_type="TK1",
minimizer="lsmr",
data_loss="linear",
use_masks=args.use_masks_srr,
# verbose=args.verbose,
)
else:
ph.print_title("Compute %s reconstruction" %
args.reconstruction_type)
SRR0 = tk.TikhonovSolver(
stacks=stacks,
reconstruction=recon0,
alpha=args.alpha,
iter_max=args.iter_max,
reg_type="TK1",
minimizer=args.minimizer,
data_loss=args.data_loss,
data_loss_scale=args.data_loss_scale,
use_masks=args.use_masks_srr,
# verbose=args.verbose,
)
SRR0.run()
recon = SRR0.get_reconstruction()
if args.reconstruction_type in ["TVL2", "HuberL2"]:
output = ph.append_to_filename(args.output, "_initTK1L2")
else:
output = args.output
if args.mask:
mask_estimator = bm.BinaryMaskFromMaskSRREstimator(recon.sitk)
mask_estimator.run()
mask_sitk = mask_estimator.get_mask_sitk()
dw.DataWriter.write_mask(mask_sitk, output)
else:
dw.DataWriter.write_image(recon.sitk, output)
if args.verbose:
show_niftis.insert(0, output)
if args.reconstruction_type in ["TVL2", "HuberL2"]:
ph.print_title("Compute %s reconstruction" %
args.reconstruction_type)
if args.tv_solver == "ADMM":
SRR = admm.ADMMSolver(
stacks=stacks,
reconstruction=st.Stack.from_stack(
SRR0.get_reconstruction()),
minimizer=args.minimizer,
alpha=args.alpha,
iter_max=args.iter_max,
rho=args.rho,
data_loss=args.data_loss,
iterations=args.iterations,
use_masks=args.use_masks_srr,
verbose=args.verbose,
)
else:
SRR = pd.PrimalDualSolver(
stacks=stacks,
reconstruction=st.Stack.from_stack(
SRR0.get_reconstruction()),
minimizer=args.minimizer,
alpha=args.alpha,
iter_max=args.iter_max,
iterations=args.iterations,
alg_type=args.pd_alg_type,
reg_type="TV"
if args.reconstruction_type == "TVL2" else "huber",
data_loss=args.data_loss,
use_masks=args.use_masks_srr,
verbose=args.verbose,
)
SRR.run()
recon = SRR.get_reconstruction()
if args.mask:
mask_estimator = bm.BinaryMaskFromMaskSRREstimator(recon.sitk)
mask_estimator.run()
mask_sitk = mask_estimator.get_mask_sitk()
dw.DataWriter.write_mask(mask_sitk, args.output)
else:
dw.DataWriter.write_image(recon.sitk, args.output)
if args.verbose:
show_niftis.insert(0, args.output)
if args.verbose:
ph.show_niftis(show_niftis, viewer=args.viewer)
ph.print_line_separator()
elapsed_time = ph.stop_timing(time_start)
ph.print_title("Summary")
print("Computational Time for Volumetric Reconstruction: %s" %
(elapsed_time))
return 0
def main():
time_start = ph.start_timing()
# Set print options
np.set_printoptions(precision=3)
pd.set_option('display.width', 1000)
input_parser = InputArgparser(description=".", )
input_parser.add_filenames()
input_parser.add_filenames_masks()
input_parser.add_dir_input_mc()
input_parser.add_suffix_mask(default="_mask")
input_parser.add_reference(required=True)
input_parser.add_reference_mask()
input_parser.add_dir_output(required=False)
input_parser.add_log_config(default=1)
input_parser.add_measures(default=["PSNR", "RMSE", "SSIM", "NCC", "NMI"])
input_parser.add_verbose(default=0)
input_parser.add_slice_thicknesses(default=None)
input_parser.add_option(option_string="--use-reference-mask",
type=int,
default=1)
input_parser.add_option(option_string="--use-slice-masks",
type=int,
default=1)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
data_reader = dr.MultipleImagesReader(
file_paths=args.filenames,
file_paths_masks=args.filenames_masks,
suffix_mask=args.suffix_mask,
dir_motion_correction=args.dir_input_mc,
stacks_slice_thicknesses=args.slice_thicknesses,
)
data_reader.read_data()
stacks = data_reader.get_data()
ph.print_info("%d input stacks read for further processing" % len(stacks))
reference = st.Stack.from_filename(args.reference, args.reference_mask)
ph.print_title("Slice Residual Similarity")
residual_evaluator = res_ev.ResidualEvaluator(
stacks=stacks,
reference=reference,
measures=args.measures,
use_reference_mask=args.use_reference_mask,
use_slice_masks=args.use_slice_masks,
)
residual_evaluator.compute_slice_projections()
residual_evaluator.evaluate_slice_similarities()
residual_evaluator.write_slice_similarities(args.dir_output)
elapsed_time = ph.stop_timing(time_start)
ph.print_title("Summary")
print("Computational Time for Slice Residual Evaluation: %s" %
(elapsed_time))
return 0
def main():
time_start = ph.start_timing()
# Set print options
np.set_printoptions(precision=3)
pd.set_option('display.width', 1000)
input_parser = InputArgparser(
description=".",
)
input_parser.add_filenames()
input_parser.add_filenames_masks()
input_parser.add_dir_input_mc()
input_parser.add_suffix_mask(default="_mask")
input_parser.add_reference(required=True)
input_parser.add_reference_mask()
input_parser.add_dir_output(required=False)
input_parser.add_log_config(default=1)
input_parser.add_measures(
default=["PSNR", "MAE", "RMSE", "SSIM", "NCC", "NMI"])
input_parser.add_verbose(default=0)
input_parser.add_target_stack(default=None)
input_parser.add_intensity_correction(default=1)
input_parser.add_slice_thicknesses(default=None)
input_parser.add_option(
option_string="--use-reference-mask", type=int, default=1)
input_parser.add_option(
option_string="--use-slice-masks", type=int, default=1)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
data_reader = dr.MultipleImagesReader(
file_paths=args.filenames,
file_paths_masks=args.filenames_masks,
suffix_mask=args.suffix_mask,
dir_motion_correction=args.dir_input_mc,
stacks_slice_thicknesses=args.slice_thicknesses,
)
data_reader.read_data()
stacks = data_reader.get_data()
ph.print_info("%d input stacks read for further processing" % len(stacks))
# Specify target stack for intensity correction and reconstruction space
if args.target_stack is None:
target_stack_index = 0
else:
filenames = ["%s.nii.gz" % s.get_filename() for s in stacks]
filename_target_stack = os.path.basename(args.target_stack)
try:
target_stack_index = filenames.index(filename_target_stack)
except ValueError as e:
raise ValueError(
"--target-stack must correspond to an image as provided by "
"--filenames")
# ---------------------------Intensity Correction--------------------------
if args.intensity_correction:
ph.print_title("Intensity Correction")
intensity_corrector = ic.IntensityCorrection()
intensity_corrector.use_individual_slice_correction(False)
intensity_corrector.use_stack_mask(True)
intensity_corrector.use_reference_mask(True)
intensity_corrector.use_verbose(False)
for i, stack in enumerate(stacks):
if i == target_stack_index:
ph.print_info("Stack %d (%s): Reference image. Skipped." % (
i + 1, stack.get_filename()))
continue
else:
ph.print_info("Stack %d (%s): Intensity Correction ... " % (
i + 1, stack.get_filename()), newline=False)
intensity_corrector.set_stack(stack)
intensity_corrector.set_reference(
stacks[target_stack_index].get_resampled_stack(
resampling_grid=stack.sitk,
interpolator="NearestNeighbor",
))
intensity_corrector.run_linear_intensity_correction()
stacks[i] = intensity_corrector.get_intensity_corrected_stack()
print("done (c1 = %g) " %
intensity_corrector.get_intensity_correction_coefficients())
# ----------------------- Slice Residual Similarity -----------------------
reference = st.Stack.from_filename(args.reference, args.reference_mask)
ph.print_title("Slice Residual Similarity")
residual_evaluator = res_ev.ResidualEvaluator(
stacks=stacks,
reference=reference,
measures=args.measures,
use_reference_mask=args.use_reference_mask,
use_slice_masks=args.use_slice_masks,
)
residual_evaluator.compute_slice_projections()
residual_evaluator.evaluate_slice_similarities()
residual_evaluator.write_slice_similarities(args.dir_output)
elapsed_time = ph.stop_timing(time_start)
ph.print_title("Summary")
print("Computational Time for Slice Residual Evaluation: %s" %
(elapsed_time))
return 0
