def main():
input_parser = InputArgparser(
description="Create and write random motion transforms.",
)
input_parser.add_dir_output(required=True)
input_parser.add_option(
option_string="--simulations", type=int, required=True)
input_parser.add_option(option_string="--angle-max", default=10)
input_parser.add_option(option_string="--translation-max", default=10)
input_parser.add_option(option_string="--seed", type=int, default=1)
input_parser.add_option(option_string="--dimension", type=int, default=3)
input_parser.add_option(
option_string="--write-settings", type=int, default=1)
input_parser.add_prefix_output(default="Euler3DTransform_")
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
motion_simulator = ms.RandomRigidMotionSimulator(
dimension=args.dimension,
angle_max_deg=args.angle_max,
translation_max=args.translation_max,
verbose=args.verbose)
motion_simulator.simulate_motion(
seed=args.seed, simulations=args.simulations)
prefix = "%sAngle%gTranslation%gSeed%d_" % (
args.prefix_output, args.angle_max, args.translation_max, args.seed)
prefix = prefix.replace(".", "p")
motion_simulator.write_transforms_sitk(
directory=args.dir_output,
prefix_filename=prefix)
return 0
def main():
input_parser = InputArgparser(
description="Create and write random rigid motion transformations. "
"Simulated transformations are exported as (Simple)ITK transforms. ",
)
input_parser.add_dir_output(required=True)
input_parser.add_option(
option_string="--simulations",
type=int,
required=True,
help="Number of simulated motion transformations."
)
input_parser.add_option(
option_string="--angle-max",
default=10,
help="random angles (in degree) are drawn such "
"that |angle| <= angle_max."
)
input_parser.add_option(
option_string="--translation-max",
default=10,
help="random translations (in millimetre) are drawn such "
"that |translation| <= translation_max."
)
input_parser.add_option(
option_string="--seed",
type=int,
default=1,
help="Seed for pseudo-random data generation"
)
input_parser.add_option(
option_string="--dimension",
type=int,
default=3,
help="Spatial dimension for transformations."
)
input_parser.add_prefix_output(default="EulerTransform_slice")
input_parser.add_verbose(default=1)
args = input_parser.parse_args()
input_parser.print_arguments(args)
motion_simulator = ms.RandomRigidMotionSimulator(
dimension=args.dimension,
angle_max_deg=args.angle_max,
translation_max=args.translation_max,
verbose=args.verbose)
motion_simulator.simulate_motion(
seed=args.seed,
simulations=args.simulations,
)
motion_simulator.write_transforms_sitk(
directory=args.dir_output,
prefix_filename=args.prefix_output,
)
return 0
def main():
time_start = ph.start_timing()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Run reconstruction pipeline including "
"(i) bias field correction, "
"(ii) volumetric reconstruction in subject space, "
"(iii) volumetric reconstruction in template space, "
"and (iv) some diagnostics to assess the obtained reconstruction.", )
input_parser.add_filenames(required=True)
input_parser.add_filenames_masks(required=True)
input_parser.add_target_stack(required=False)
input_parser.add_suffix_mask(default="")
input_parser.add_dir_output(required=True)
input_parser.add_alpha(default=0.01)
input_parser.add_verbose(default=0)
input_parser.add_gestational_age(required=False)
input_parser.add_prefix_output(default="")
input_parser.add_search_angle(default=180)
input_parser.add_multiresolution(default=0)
input_parser.add_log_config(default=1)
input_parser.add_isotropic_resolution()
input_parser.add_reference()
input_parser.add_reference_mask()
input_parser.add_bias_field_correction(default=1)
input_parser.add_intensity_correction(default=1)
input_parser.add_iter_max(default=10)
input_parser.add_two_step_cycles(default=3)
input_parser.add_slice_thicknesses(default=None)
input_parser.add_option(
option_string="--run-bias-field-correction",
type=int,
help="Turn on/off bias field correction. "
"If off, it is assumed that this step was already performed "
"if --bias-field-correction is active.",
default=1)
input_parser.add_option(
option_string="--run-recon-subject-space",
type=int,
help="Turn on/off reconstruction in subject space. "
"If off, it is assumed that this step was already performed.",
default=1)
input_parser.add_option(
option_string="--run-recon-template-space",
type=int,
help="Turn on/off reconstruction in template space. "
"If off, it is assumed that this step was already performed.",
default=1)
input_parser.add_option(
option_string="--run-diagnostics",
type=int,
help="Turn on/off diagnostics of the obtained volumetric "
"reconstruction. ",
default=0)
input_parser.add_option(
option_string="--initial-transform",
type=str,
help="Set initial transform to be used for register_image.",
default=None)
input_parser.add_outlier_rejection(default=1)
input_parser.add_threshold_first(default=0.5)
input_parser.add_threshold(default=0.8)
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")
input_parser.add_argument(
"--v2v-robust",
"-v2v-robust",
action='store_true',
help="If given, a more robust volume-to-volume registration step is "
"performed, i.e. four rigid registrations are performed using four "
"rigid transform initializations based on "
"principal component alignment of associated masks.")
input_parser.add_interleave(default=3)
input_parser.add_argument(
"--s2v-hierarchical",
"-s2v-hierarchical",
action='store_true',
help="If given, a hierarchical approach for the first slice-to-volume "
"registration cycle is used, i.e. sub-packages defined by the "
"specified interleave (--interleave) are registered until each "
"slice is registered independently.")
args = input_parser.parse_args()
input_parser.print_arguments(args)
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
filename_srr = "srr"
dir_output_preprocessing = os.path.join(args.dir_output,
"preprocessing_n4itk")
dir_output_recon_subject_space = os.path.join(args.dir_output,
"recon_subject_space")
dir_output_recon_template_space = os.path.join(args.dir_output,
"recon_template_space")
dir_output_diagnostics = os.path.join(args.dir_output, "diagnostics")
srr_subject = os.path.join(dir_output_recon_subject_space,
"%s_subject.nii.gz" % filename_srr)
srr_subject_mask = ph.append_to_filename(srr_subject, "_mask")
srr_template = os.path.join(dir_output_recon_template_space,
"%s_template.nii.gz" % filename_srr)
srr_template_mask = ph.append_to_filename(srr_template, "_mask")
trafo_template = os.path.join(dir_output_recon_template_space,
"registration_transform_sitk.txt")
srr_slice_coverage = os.path.join(
dir_output_diagnostics,
"%s_template_slicecoverage.nii.gz" % filename_srr)
if args.bias_field_correction and args.run_bias_field_correction:
for i, f in enumerate(args.filenames):
output = os.path.join(dir_output_preprocessing,
os.path.basename(f))
cmd_args = []
cmd_args.append("--filename '%s'" % f)
cmd_args.append("--filename-mask '%s'" % args.filenames_masks[i])
cmd_args.append("--output '%s'" % output)
# cmd_args.append("--verbose %d" % args.verbose)
cmd_args.append("--log-config %d" % args.log_config)
cmd = "niftymic_correct_bias_field %s" % (" ").join(cmd_args)
time_start_bias = ph.start_timing()
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Bias field correction failed")
elapsed_time_bias = ph.stop_timing(time_start_bias)
filenames = [
os.path.join(dir_output_preprocessing, os.path.basename(f))
for f in args.filenames
]
elif args.bias_field_correction and not args.run_bias_field_correction:
elapsed_time_bias = ph.get_zero_time()
filenames = [
os.path.join(dir_output_preprocessing, os.path.basename(f))
for f in args.filenames
]
else:
elapsed_time_bias = ph.get_zero_time()
filenames = args.filenames
# Specify target stack for intensity correction and reconstruction space
if args.target_stack is None:
target_stack = filenames[0]
else:
try:
target_stack_index = args.filenames.index(args.target_stack)
except ValueError as e:
raise ValueError(
"--target-stack must correspond to an image as provided by "
"--filenames")
target_stack = filenames[target_stack_index]
# Add single quotes around individual filenames to account for whitespaces
filenames = ["'" + f + "'" for f in filenames]
filenames_masks = ["'" + f + "'" for f in args.filenames_masks]
if args.run_recon_subject_space:
cmd_args = ["niftymic_reconstruct_volume"]
cmd_args.append("--filenames %s" % (" ").join(filenames))
cmd_args.append("--filenames-masks %s" % (" ").join(filenames_masks))
cmd_args.append("--multiresolution %d" % args.multiresolution)
cmd_args.append("--target-stack '%s'" % target_stack)
cmd_args.append("--output '%s'" % srr_subject)
cmd_args.append("--suffix-mask '%s'" % args.suffix_mask)
cmd_args.append("--intensity-correction %d" %
args.intensity_correction)
cmd_args.append("--alpha %s" % args.alpha)
cmd_args.append("--iter-max %d" % args.iter_max)
cmd_args.append("--two-step-cycles %d" % args.two_step_cycles)
cmd_args.append("--outlier-rejection %d" % args.outlier_rejection)
cmd_args.append("--threshold-first %f" % args.threshold_first)
cmd_args.append("--threshold %f" % args.threshold)
if args.slice_thicknesses is not None:
cmd_args.append("--slice-thicknesses %s" %
" ".join(map(str, args.slice_thicknesses)))
cmd_args.append("--verbose %d" % args.verbose)
cmd_args.append("--log-config %d" % args.log_config)
if args.isotropic_resolution is not None:
cmd_args.append("--isotropic-resolution %f" %
args.isotropic_resolution)
if args.reference is not None:
cmd_args.append("--reference %s" % args.reference)
if args.reference_mask is not None:
cmd_args.append("--reference-mask %s" % args.reference_mask)
if args.sda:
cmd_args.append("--sda")
if args.v2v_robust:
cmd_args.append("--v2v-robust")
if args.s2v_hierarchical:
cmd_args.append("--s2v-hierarchical")
cmd = (" ").join(cmd_args)
time_start_volrec = ph.start_timing()
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Reconstruction in subject space failed")
# Compute SRR mask in subject space
# (Approximated using SDA within reconstruct_volume)
if 0:
dir_motion_correction = os.path.join(
dir_output_recon_subject_space, "motion_correction")
cmd_args = ["niftymic_reconstruct_volume_from_slices"]
cmd_args.append("--filenames %s" % " ".join(filenames_masks))
cmd_args.append("--dir-input-mc '%s'" % dir_motion_correction)
cmd_args.append("--output '%s'" % srr_subject_mask)
cmd_args.append("--reconstruction-space '%s'" % srr_subject)
cmd_args.append("--suffix-mask '%s'" % args.suffix_mask)
cmd_args.append("--mask")
cmd_args.append("--log-config %d" % args.log_config)
if args.slice_thicknesses is not None:
cmd_args.append("--slice-thicknesses %s" %
" ".join(map(str, args.slice_thicknesses)))
if args.sda:
cmd_args.append("--sda")
cmd_args.append("--alpha 1")
else:
cmd_args.append("--alpha 0.1")
cmd_args.append("--iter-max 5")
cmd = (" ").join(cmd_args)
ph.execute_command(cmd)
elapsed_time_volrec = ph.stop_timing(time_start_volrec)
else:
elapsed_time_volrec = ph.get_zero_time()
if args.run_recon_template_space:
if args.gestational_age is None:
template_stack_estimator = \
tse.TemplateStackEstimator.from_mask(srr_subject_mask)
gestational_age = template_stack_estimator.get_estimated_gw()
ph.print_info("Estimated gestational age: %d" % gestational_age)
else:
gestational_age = args.gestational_age
template = os.path.join(DIR_TEMPLATES,
"STA%d.nii.gz" % gestational_age)
template_mask = os.path.join(DIR_TEMPLATES,
"STA%d_mask.nii.gz" % gestational_age)
# Register SRR to template space
cmd_args = ["niftymic_register_image"]
cmd_args.append("--fixed '%s'" % template)
cmd_args.append("--moving '%s'" % srr_subject)
cmd_args.append("--fixed-mask '%s'" % template_mask)
cmd_args.append("--moving-mask '%s'" % srr_subject_mask)
cmd_args.append(
"--dir-input-mc '%s'" %
os.path.join(dir_output_recon_subject_space, "motion_correction"))
cmd_args.append("--output '%s'" % trafo_template)
cmd_args.append("--verbose %s" % args.verbose)
cmd_args.append("--log-config %d" % args.log_config)
cmd_args.append("--refine-pca")
if args.initial_transform is not None:
cmd_args.append("--initial-transform '%s'" %
args.initial_transform)
cmd = (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Registration to template space failed")
# Compute SRR in template space
dir_input_mc = os.path.join(dir_output_recon_template_space,
"motion_correction")
cmd_args = ["niftymic_reconstruct_volume_from_slices"]
cmd_args.append("--filenames %s" % (" ").join(filenames))
cmd_args.append("--filenames-masks %s" % (" ").join(filenames_masks))
cmd_args.append("--dir-input-mc '%s'" % dir_input_mc)
cmd_args.append("--output '%s'" % srr_template)
cmd_args.append("--reconstruction-space '%s'" % template)
cmd_args.append("--target-stack '%s'" % target_stack)
cmd_args.append("--iter-max %d" % args.iter_max)
cmd_args.append("--alpha %s" % args.alpha)
cmd_args.append("--suffix-mask '%s'" % args.suffix_mask)
cmd_args.append("--verbose %s" % args.verbose)
cmd_args.append("--log-config %d" % args.log_config)
if args.slice_thicknesses is not None:
cmd_args.append("--slice-thicknesses %s" %
" ".join(map(str, args.slice_thicknesses)))
if args.sda:
cmd_args.append("--sda")
cmd = (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Reconstruction in template space failed")
# Compute SRR mask in template space
if 1:
dir_motion_correction = os.path.join(
dir_output_recon_template_space, "motion_correction")
cmd_args = ["niftymic_reconstruct_volume_from_slices"]
cmd_args.append("--filenames %s" % " ".join(filenames_masks))
cmd_args.append("--dir-input-mc '%s'" % dir_motion_correction)
cmd_args.append("--output '%s'" % srr_template_mask)
cmd_args.append("--reconstruction-space '%s'" % srr_template)
cmd_args.append("--suffix-mask '%s'" % args.suffix_mask)
cmd_args.append("--log-config %d" % args.log_config)
cmd_args.append("--mask")
if args.slice_thicknesses is not None:
cmd_args.append("--slice-thicknesses %s" %
" ".join(map(str, args.slice_thicknesses)))
if args.sda:
cmd_args.append("--sda")
cmd_args.append("--alpha 1")
else:
cmd_args.append("--alpha 0.1")
cmd_args.append("--iter-max 5")
cmd = (" ").join(cmd_args)
ph.execute_command(cmd)
# Copy SRR to output directory
if 0:
output = "%sSRR_Stacks%d.nii.gz" % (args.prefix_output,
len(args.filenames))
path_to_output = os.path.join(args.dir_output, output)
cmd = "cp -p '%s' '%s'" % (srr_template, path_to_output)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Copy of SRR to output directory failed")
# Multiply template mask with reconstruction
if 0:
cmd_args = ["niftymic_multiply"]
fnames = [
srr_template,
srr_template_mask,
]
output_masked = "Masked_%s" % output
path_to_output_masked = os.path.join(args.dir_output,
output_masked)
cmd_args.append("--filenames %s" % " ".join(fnames))
cmd_args.append("--output '%s'" % path_to_output_masked)
cmd = (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("SRR brain masking failed")
else:
elapsed_time_template = ph.get_zero_time()
if args.run_diagnostics:
dir_input_mc = os.path.join(dir_output_recon_template_space,
"motion_correction")
dir_output_orig_vs_proj = os.path.join(dir_output_diagnostics,
"original_vs_projected")
dir_output_selfsimilarity = os.path.join(dir_output_diagnostics,
"selfsimilarity")
dir_output_orig_vs_proj_pdf = os.path.join(dir_output_orig_vs_proj,
"pdf")
# Show slice coverage over reconstruction space
exe = os.path.abspath(show_slice_coverage.__file__)
cmd_args = ["python %s" % exe]
cmd_args.append("--filenames %s" % (" ").join(filenames))
cmd_args.append("--dir-input-mc '%s'" % dir_input_mc)
cmd_args.append("--reconstruction-space '%s'" % srr_template)
cmd_args.append("--output '%s'" % srr_slice_coverage)
cmd = (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Slice coverage visualization failed")
# Get simulated/projected slices
exe = os.path.abspath(simulate_stacks_from_reconstruction.__file__)
cmd_args = ["python %s" % exe]
cmd_args.append("--filenames %s" % (" ").join(filenames))
if args.filenames_masks is not None:
cmd_args.append("--filenames-masks %s" %
(" ").join(filenames_masks))
cmd_args.append("--dir-input-mc '%s'" % dir_input_mc)
cmd_args.append("--dir-output '%s'" % dir_output_orig_vs_proj)
cmd_args.append("--reconstruction '%s'" % srr_template)
cmd_args.append("--copy-data 1")
if args.slice_thicknesses is not None:
cmd_args.append("--slice-thicknesses %s" %
" ".join(map(str, args.slice_thicknesses)))
# cmd_args.append("--verbose %s" % args.verbose)
cmd = (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("SRR slice projections failed")
filenames_simulated = [
"'%s" % os.path.join(dir_output_orig_vs_proj, os.path.basename(f))
for f in filenames
]
# Evaluate slice similarities to ground truth
exe = os.path.abspath(evaluate_simulated_stack_similarity.__file__)
cmd_args = ["python %s" % exe]
cmd_args.append("--filenames %s" % (" ").join(filenames_simulated))
if args.filenames_masks is not None:
cmd_args.append("--filenames-masks %s" %
(" ").join(filenames_masks))
cmd_args.append("--measures NCC SSIM")
cmd_args.append("--dir-output '%s'" % dir_output_selfsimilarity)
cmd = (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Evaluation of stack similarities failed")
# Generate figures showing the quantitative comparison
exe = os.path.abspath(
show_evaluated_simulated_stack_similarity.__file__)
cmd_args = ["python %s" % exe]
cmd_args.append("--dir-input '%s'" % dir_output_selfsimilarity)
cmd_args.append("--dir-output '%s'" % dir_output_selfsimilarity)
cmd = (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
ph.print_warning("Visualization of stack similarities failed")
# Generate pdfs showing all the side-by-side comparisons
if 0:
exe = os.path.abspath(
export_side_by_side_simulated_vs_original_slice_comparison.
__file__)
cmd_args = ["python %s" % exe]
cmd_args.append("--filenames %s" % (" ").join(filenames_simulated))
cmd_args.append("--dir-output '%s'" % dir_output_orig_vs_proj_pdf)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
cmd = (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Generation of PDF overview failed")
ph.print_title("Summary")
print("Computational Time for Bias Field Correction: %s" %
elapsed_time_bias)
print("Computational Time for Volumetric Reconstruction: %s" %
elapsed_time_volrec)
print("Computational Time for Pipeline: %s" % ph.stop_timing(time_start))
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="Perform (linear) intensity correction across "
"stacks/images given a reference stack/image", )
input_parser.add_filenames(required=True)
input_parser.add_dir_output(required=True)
input_parser.add_reference(required=True)
input_parser.add_suffix_mask(default="_mask")
input_parser.add_search_angle(default=180)
input_parser.add_prefix_output(default="IC_")
input_parser.add_log_config(default=1)
input_parser.add_option(
option_string="--registration",
type=int,
help="Turn on/off registration from image to reference prior to "
"intensity correction.",
default=0)
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
if args.reference in args.filenames:
args.filenames.remove(args.reference)
# Read data
data_reader = dr.MultipleImagesReader(args.filenames,
suffix_mask=args.suffix_mask,
extract_slices=False)
data_reader.read_data()
stacks = data_reader.get_data()
data_reader = dr.MultipleImagesReader([args.reference],
suffix_mask=args.suffix_mask,
extract_slices=False)
data_reader.read_data()
reference = data_reader.get_data()[0]
if args.registration:
# 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)
registration = regflirt.FLIRT(
moving=reference,
registration_type="Rigid",
use_fixed_mask=True,
use_moving_mask=True,
options=search_angles,
use_verbose=False,
)
# Perform Intensity Correction
ph.print_title("Perform Intensity Correction")
intensity_corrector = ic.IntensityCorrection(
use_reference_mask=True,
use_individual_slice_correction=False,
prefix_corrected=args.prefix_output,
use_verbose=False,
)
stacks_corrected = [None] * len(stacks)
for i, stack in enumerate(stacks):
if args.registration:
ph.print_info("Image %d/%d: Registration ... " %
(i + 1, len(stacks)),
newline=False)
registration.set_fixed(stack)
registration.run()
transform_sitk = registration.get_registration_transform_sitk()
stack.update_motion_correction(transform_sitk)
print("done")
ph.print_info("Image %d/%d: Intensity Correction ... " %
(i + 1, len(stacks)),
newline=False)
ref = reference.get_resampled_stack(stack.sitk)
ref = st.Stack.from_sitk_image(image_sitk=ref.sitk,
image_sitk_mask=stack.sitk_mask *
ref.sitk_mask,
filename=reference.get_filename())
intensity_corrector.set_stack(stack)
intensity_corrector.set_reference(ref)
intensity_corrector.run_linear_intensity_correction()
# intensity_corrector.run_affine_intensity_correction()
stacks_corrected[i] = \
intensity_corrector.get_intensity_corrected_stack()
print("done (c1 = %g) " %
intensity_corrector.get_intensity_correction_coefficients())
# Write Data
stacks_corrected[i].write(args.dir_output,
write_mask=True,
suffix_mask=args.suffix_mask)
if args.verbose:
sitkh.show_stacks(
[
reference,
stacks_corrected[i],
# stacks[i],
],
segmentation=stacks_corrected[i])
# ph.pause()
# Write reference too (although not intensity corrected)
reference.write(args.dir_output,
filename=args.prefix_output + reference.get_filename(),
write_mask=True,
suffix_mask=args.suffix_mask)
elapsed_time = ph.stop_timing(time_start)
ph.print_title("Summary")
print("Computational Time for Intensity Correction(s): %s" %
(elapsed_time))
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_dir_input()
input_parser.add_filenames()
input_parser.add_image_selection()
input_parser.add_dir_output(required=True)
input_parser.add_prefix_output(default="SRR_")
input_parser.add_suffix_mask(default="_mask")
input_parser.add_target_stack_index(default=0)
input_parser.add_extra_frame_target(default=10)
input_parser.add_isotropic_resolution(default=None)
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=0.02 # 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_subfolder_comparison()
input_parser.add_provide_comparison(default=0)
input_parser.add_log_script_execution(default=1)
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
# Write script execution call
if args.log_script_execution:
input_parser.write_performed_script_execution(
os.path.abspath(__file__))
# --------------------------------Read Data--------------------------------
ph.print_title("Read Data")
# Neither '--dir-input' nor '--filenames' was specified
if args.filenames is not None and args.dir_input is not None:
raise IOError("Provide input by either '--dir-input' or '--filenames' "
"but not both together")
# '--dir-input' specified
elif args.dir_input is not None:
data_reader = dr.ImageSlicesDirectoryReader(
path_to_directory=args.dir_input,
suffix_mask=args.suffix_mask,
image_selection=args.image_selection)
# '--filenames' specified
elif args.filenames is not None:
data_reader = dr.MultipleImagesReader(args.filenames,
suffix_mask=args.suffix_mask)
else:
raise IOError("Provide input by either '--dir-input' or '--filenames'")
if args.reconstruction_type not in ["TK1L2", "TVL2", "HuberL2"]:
raise IOError("Reconstruction type unknown")
data_reader.read_data()
stacks = data_reader.get_data()
ph.print_info("%d input stacks read for further processing" % len(stacks))
# Reconstruction space is given isotropically resampled target stack
if args.reconstruction_space is None:
recon0 = \
stacks[args.target_stack_index].get_isotropically_resampled_stack(
resolution=args.isotropic_resolution,
extra_frame=args.extra_frame_target)
# 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[args.target_stack_index].get_resampled_stack(recon0.sitk)
recon0 = recon0.get_stack_multiplied_with_mask()
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=args.iter_max,
reg_type="TK1",
minimizer=args.minimizer,
data_loss=args.data_loss,
data_loss_scale=args.data_loss_scale,
# verbose=args.verbose,
)
SRR0.run()
recon = SRR0.get_reconstruction()
recon.set_filename(SRR0.get_setting_specific_filename(args.prefix_output))
recon.write(args.dir_output)
# List to store SRRs
recons = []
for i in range(0, len(stacks)):
recons.append(stacks[i])
recons.insert(0, recon)
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,
verbose=args.verbose,
)
SRR.run()
recon = SRR.get_reconstruction()
recon.set_filename(
SRR.get_setting_specific_filename(args.prefix_output))
recons.insert(0, recon)
recon.write(args.dir_output)
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,
verbose=args.verbose,
)
SRR.run()
recon = SRR.get_reconstruction()
recon.set_filename(
SRR.get_setting_specific_filename(args.prefix_output))
recons.insert(0, recon)
recon.write(args.dir_output)
if args.verbose and not args.provide_comparison:
sitkh.show_stacks(recons)
# Show SRR together with linearly resampled input data.
# Additionally, a script is generated to open files
if args.provide_comparison:
sitkh.show_stacks(
recons,
show_comparison_file=args.provide_comparison,
dir_output=os.path.join(args.dir_output,
args.subfolder_comparison),
)
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()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Perform Bias Field correction on images based on N4ITK.",
)
input_parser.add_filenames(required=True)
input_parser.add_dir_output(required=True)
input_parser.add_suffix_mask(default="_mask")
input_parser.add_prefix_output(default="N4ITK_")
input_parser.add_option(
option_string="--convergence-threshold",
type=float,
help="Specify the convergence threshold.",
default=1e-6,
)
input_parser.add_option(
option_string="--spline-order",
type=int,
help="Specify the spline order defining the bias field estimate.",
default=3,
)
input_parser.add_option(
option_string="--wiener-filter-noise",
type=float,
help="Specify the noise estimate defining the Wiener filter.",
default=0.11,
)
input_parser.add_option(
option_string="--bias-field-fwhm",
type=float,
help="Specify the full width at half maximum parameter characterizing "
"the width of the Gaussian deconvolution.",
default=0.15,
)
input_parser.add_log_script_execution(default=1)
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
# Write script execution call
if args.log_script_execution:
input_parser.write_performed_script_execution(
os.path.abspath(__file__))
# Read data
data_reader = dr.MultipleImagesReader(args.filenames,
suffix_mask=args.suffix_mask)
data_reader.read_data()
stacks = data_reader.get_data()
# Perform Bias Field Correction
ph.print_title("Perform Bias Field Correction")
bias_field_corrector = n4itk.N4BiasFieldCorrection(
convergence_threshold=args.convergence_threshold,
spline_order=args.spline_order,
wiener_filter_noise=args.wiener_filter_noise,
bias_field_fwhm=args.bias_field_fwhm,
prefix_corrected=args.prefix_output,
)
stacks_corrected = [None] * len(stacks)
for i, stack in enumerate(stacks):
ph.print_info("Image %d/%d: N4ITK Bias Field Correction ... " %
(i + 1, len(stacks)),
newline=False)
bias_field_corrector.set_stack(stack)
bias_field_corrector.run_bias_field_correction()
stacks_corrected[i] = \
bias_field_corrector.get_bias_field_corrected_stack()
print("done")
ph.print_info("Image %d/%d: Computational time = %s" %
(i + 1, len(stacks),
bias_field_corrector.get_computational_time()))
# Write Data
stacks_corrected[i].write(args.dir_output,
write_mask=True,
suffix_mask=args.suffix_mask)
if args.verbose:
sitkh.show_stacks([stacks[i], stacks_corrected[i]],
segmentation=stacks[i])
elapsed_time = ph.stop_timing(time_start)
ph.print_title("Summary")
print("Computational Time for Bias Field Correction(s): %s" %
(elapsed_time))
return 0
def main():
time_start = ph.start_timing()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Run reconstruction pipeline including "
"(i) bias field correction, "
"(ii) volumetric reconstruction in subject space, "
"and (iii) volumetric reconstruction in template space.", )
input_parser.add_filenames(required=True)
input_parser.add_filenames_masks(required=True)
input_parser.add_target_stack(required=False)
input_parser.add_suffix_mask(default="''")
input_parser.add_dir_output(required=True)
input_parser.add_alpha(default=0.01)
input_parser.add_verbose(default=0)
input_parser.add_gestational_age(required=False)
input_parser.add_prefix_output(default="")
input_parser.add_search_angle(default=180)
input_parser.add_multiresolution(default=0)
input_parser.add_log_config(default=1)
input_parser.add_isotropic_resolution()
input_parser.add_reference()
input_parser.add_reference_mask()
input_parser.add_bias_field_correction(default=1)
input_parser.add_intensity_correction(default=1)
input_parser.add_iter_max(default=10)
input_parser.add_two_step_cycles(default=3)
input_parser.add_option(
option_string="--run-bias-field-correction",
type=int,
help="Turn on/off bias field correction. "
"If off, it is assumed that this step was already performed",
default=1)
input_parser.add_option(
option_string="--run-recon-subject-space",
type=int,
help="Turn on/off reconstruction in subject space. "
"If off, it is assumed that this step was already performed",
default=1)
input_parser.add_option(
option_string="--run-recon-template-space",
type=int,
help="Turn on/off reconstruction in template space. "
"If off, it is assumed that this step was already performed",
default=1)
input_parser.add_option(
option_string="--run-data-vs-simulated-data",
type=int,
help="Turn on/off comparison of data vs data simulated from the "
"obtained volumetric reconstruction. "
"If off, it is assumed that this step was already performed",
default=0)
input_parser.add_option(
option_string="--initial-transform",
type=str,
help="Set initial transform to be used for register_image.",
default=None)
input_parser.add_outlier_rejection(default=1)
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.log_config:
input_parser.log_config(os.path.abspath(__file__))
filename_srr = "srr"
dir_output_preprocessing = os.path.join(args.dir_output,
"preprocessing_n4itk")
dir_output_recon_subject_space = os.path.join(args.dir_output,
"recon_subject_space")
dir_output_recon_template_space = os.path.join(args.dir_output,
"recon_template_space")
dir_output_data_vs_simulatd_data = os.path.join(args.dir_output,
"data_vs_simulated_data")
srr_subject = os.path.join(dir_output_recon_subject_space,
"%s_subject.nii.gz" % filename_srr)
srr_subject_mask = ph.append_to_filename(srr_subject, "_mask")
srr_template = os.path.join(dir_output_recon_template_space,
"%s_template.nii.gz" % filename_srr)
srr_template_mask = ph.append_to_filename(srr_template, "_mask")
trafo_template = os.path.join(dir_output_recon_template_space,
"registration_transform_sitk.txt")
if args.target_stack is None:
target_stack = args.filenames[0]
else:
target_stack = args.target_stack
if args.bias_field_correction and args.run_bias_field_correction:
for i, f in enumerate(args.filenames):
output = os.path.join(dir_output_preprocessing,
os.path.basename(f))
cmd_args = []
cmd_args.append("--filename %s" % f)
cmd_args.append("--filename-mask %s" % args.filenames_masks[i])
cmd_args.append("--output %s" % output)
# cmd_args.append("--verbose %d" % args.verbose)
cmd = "niftymic_correct_bias_field %s" % (" ").join(cmd_args)
time_start_bias = ph.start_timing()
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Bias field correction failed")
elapsed_time_bias = ph.stop_timing(time_start_bias)
filenames = [
os.path.join(dir_output_preprocessing, os.path.basename(f))
for f in args.filenames
]
elif args.bias_field_correction and not args.run_bias_field_correction:
elapsed_time_bias = ph.get_zero_time()
filenames = [
os.path.join(dir_output_preprocessing, os.path.basename(f))
for f in args.filenames
]
else:
elapsed_time_bias = ph.get_zero_time()
filenames = args.filenames
if args.run_recon_subject_space:
target_stack_index = args.filenames.index(target_stack)
cmd_args = []
cmd_args.append("--filenames %s" % (" ").join(filenames))
if args.filenames_masks is not None:
cmd_args.append("--filenames-masks %s" %
(" ").join(args.filenames_masks))
cmd_args.append("--multiresolution %d" % args.multiresolution)
cmd_args.append("--target-stack-index %d" % target_stack_index)
cmd_args.append("--output %s" % srr_subject)
cmd_args.append("--suffix-mask '%s'" % args.suffix_mask)
cmd_args.append("--intensity-correction %d" %
args.intensity_correction)
cmd_args.append("--alpha %s" % args.alpha)
cmd_args.append("--iter-max %d" % args.iter_max)
cmd_args.append("--two-step-cycles %d" % args.two_step_cycles)
cmd_args.append("--outlier-rejection %d" % args.outlier_rejection)
cmd_args.append("--verbose %d" % args.verbose)
if args.isotropic_resolution is not None:
cmd_args.append("--isotropic-resolution %f" %
args.isotropic_resolution)
if args.reference is not None:
cmd_args.append("--reference %s" % args.reference)
if args.reference_mask is not None:
cmd_args.append("--reference-mask %s" % args.reference_mask)
if args.sda:
cmd_args.append("--sda")
cmd = "niftymic_reconstruct_volume %s" % (" ").join(cmd_args)
time_start_volrec = ph.start_timing()
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Reconstruction in subject space failed")
# Compute SRR mask in subject space
# (Approximated using SDA within reconstruct_volume)
if 0:
dir_motion_correction = os.path.join(
dir_output_recon_subject_space, "motion_correction")
cmd_args = ["niftymic_reconstruct_volume_from_slices"]
cmd_args.append("--filenames %s" % " ".join(args.filenames_masks))
cmd_args.append("--dir-input-mc %s" % dir_motion_correction)
cmd_args.append("--output %s" % srr_subject_mask)
cmd_args.append("--reconstruction-space %s" % srr_subject)
cmd_args.append("--suffix-mask '%s'" % args.suffix_mask)
cmd_args.append("--mask")
if args.sda:
cmd_args.append("--sda")
cmd_args.append("--alpha 1")
else:
cmd_args.append("--alpha 0.1")
cmd_args.append("--iter-max 5")
cmd = (" ").join(cmd_args)
ph.execute_command(cmd)
elapsed_time_volrec = ph.stop_timing(time_start_volrec)
else:
elapsed_time_volrec = ph.get_zero_time()
if args.run_recon_template_space:
if args.gestational_age is None:
template_stack_estimator = \
tse.TemplateStackEstimator.from_mask(srr_subject_mask)
gestational_age = template_stack_estimator.get_estimated_gw()
ph.print_info("Estimated gestational age: %d" % gestational_age)
else:
gestational_age = args.gestational_age
template = os.path.join(DIR_TEMPLATES,
"STA%d.nii.gz" % gestational_age)
template_mask = os.path.join(DIR_TEMPLATES,
"STA%d_mask.nii.gz" % gestational_age)
cmd_args = []
cmd_args.append("--fixed %s" % template)
cmd_args.append("--moving %s" % srr_subject)
cmd_args.append("--fixed-mask %s" % template_mask)
cmd_args.append("--moving-mask %s" % srr_subject_mask)
cmd_args.append(
"--dir-input-mc %s" %
os.path.join(dir_output_recon_subject_space, "motion_correction"))
cmd_args.append("--output %s" % trafo_template)
cmd_args.append("--verbose %s" % args.verbose)
if args.initial_transform is not None:
cmd_args.append("--initial-transform %s" % args.initial_transform)
cmd_args.append("--use-flirt 0")
cmd_args.append("--test-ap-flip 0")
cmd = "niftymic_register_image %s" % (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Registration to template space failed")
# reconstruct volume in template space
# pattern = "[a-zA-Z0-9_.]+(ResamplingToTemplateSpace.nii.gz)"
# p = re.compile(pattern)
# reconstruction_space = [
# os.path.join(dir_output_recon_template_space, p.match(f).group(0))
# for f in os.listdir(dir_output_recon_template_space)
# if p.match(f)][0]
dir_input_mc = os.path.join(dir_output_recon_template_space,
"motion_correction")
cmd_args = ["niftymic_reconstruct_volume_from_slices"]
cmd_args.append("--filenames %s" % (" ").join(filenames))
cmd_args.append("--dir-input-mc %s" % dir_input_mc)
cmd_args.append("--output %s" % srr_template)
cmd_args.append("--reconstruction-space %s" % template)
cmd_args.append("--iter-max %d" % args.iter_max)
cmd_args.append("--alpha %s" % args.alpha)
cmd_args.append("--suffix-mask '%s'" % args.suffix_mask)
cmd_args.append("--verbose %s" % args.verbose)
if args.sda:
cmd_args.append("--sda")
cmd = (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Reconstruction in template space failed")
# Compute SRR mask in template space
if 1:
dir_motion_correction = os.path.join(
dir_output_recon_template_space, "motion_correction")
cmd_args = ["niftymic_reconstruct_volume_from_slices"]
cmd_args.append("--filenames %s" % " ".join(args.filenames_masks))
cmd_args.append("--dir-input-mc %s" % dir_motion_correction)
cmd_args.append("--output %s" % srr_template_mask)
cmd_args.append("--reconstruction-space %s" % srr_template)
cmd_args.append("--suffix-mask '%s'" % args.suffix_mask)
cmd_args.append("--mask")
if args.sda:
cmd_args.append("--sda")
cmd_args.append("--alpha 1")
else:
cmd_args.append("--alpha 0.1")
cmd_args.append("--iter-max 5")
cmd = (" ").join(cmd_args)
ph.execute_command(cmd)
# Copy SRR to output directory
output = "%sSRR_Stacks%d.nii.gz" % (args.prefix_output,
len(args.filenames))
path_to_output = os.path.join(args.dir_output, output)
cmd = "cp -p %s %s" % (srr_template, path_to_output)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Copy of SRR to output directory failed")
# Multiply template mask with reconstruction
cmd_args = ["niftymic_multiply"]
fnames = [
srr_template,
srr_template_mask,
]
output_masked = "Masked_%s" % output
path_to_output_masked = os.path.join(args.dir_output, output_masked)
cmd_args.append("--filenames %s" % " ".join(fnames))
cmd_args.append("--output %s" % path_to_output_masked)
cmd = (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("SRR brain masking failed")
else:
elapsed_time_template = ph.get_zero_time()
if args.run_data_vs_simulated_data:
dir_input_mc = os.path.join(dir_output_recon_template_space,
"motion_correction")
# Get simulated/projected slices
cmd_args = []
cmd_args.append("--filenames %s" % (" ").join(filenames))
if args.filenames_masks is not None:
cmd_args.append("--filenames-masks %s" %
(" ").join(args.filenames_masks))
cmd_args.append("--dir-input-mc %s" % dir_input_mc)
cmd_args.append("--dir-output %s" % dir_output_data_vs_simulatd_data)
cmd_args.append("--reconstruction %s" % srr_template)
cmd_args.append("--copy-data 1")
cmd_args.append("--suffix-mask '%s'" % args.suffix_mask)
# cmd_args.append("--verbose %s" % args.verbose)
exe = os.path.abspath(simulate_stacks_from_reconstruction.__file__)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("SRR slice projections failed")
filenames_simulated = [
os.path.join(dir_output_data_vs_simulatd_data, os.path.basename(f))
for f in filenames
]
dir_output_evaluation = os.path.join(dir_output_data_vs_simulatd_data,
"evaluation")
dir_output_figures = os.path.join(dir_output_data_vs_simulatd_data,
"figures")
dir_output_side_by_side = os.path.join(dir_output_figures,
"side-by-side")
# Evaluate slice similarities to ground truth
cmd_args = []
cmd_args.append("--filenames %s" % (" ").join(filenames_simulated))
if args.filenames_masks is not None:
cmd_args.append("--filenames-masks %s" %
(" ").join(args.filenames_masks))
cmd_args.append("--suffix-mask '%s'" % args.suffix_mask)
cmd_args.append("--measures NCC SSIM")
cmd_args.append("--dir-output %s" % dir_output_evaluation)
exe = os.path.abspath(evaluate_simulated_stack_similarity.__file__)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Evaluation of slice similarities failed")
# Generate figures showing the quantitative comparison
cmd_args = []
cmd_args.append("--dir-input %s" % dir_output_evaluation)
cmd_args.append("--dir-output %s" % dir_output_figures)
exe = os.path.abspath(
show_evaluated_simulated_stack_similarity.__file__)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
ph.print_warning("Visualization of slice similarities failed")
# Generate pdfs showing all the side-by-side comparisons
cmd_args = []
cmd_args.append("--filenames %s" % (" ").join(filenames_simulated))
cmd_args.append("--dir-output %s" % dir_output_side_by_side)
exe = os.path.abspath(
export_side_by_side_simulated_vs_original_slice_comparison.__file__
)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Generation of PDF overview failed")
ph.print_title("Summary")
print("Computational Time for Bias Field Correction: %s" %
elapsed_time_bias)
print("Computational Time for Volumetric Reconstruction: %s" %
elapsed_time_volrec)
print("Computational Time for Pipeline: %s" % ph.stop_timing(time_start))
return 0
def main():
time_start = ph.start_timing()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Run reconstruction pipeline including "
"(i) preprocessing (bias field correction + intensity correction), "
"(ii) volumetric reconstruction in subject space, "
"and (iii) volumetric reconstruction in template space.",
)
input_parser.add_dir_input(required=True)
input_parser.add_dir_mask(required=True)
input_parser.add_dir_output(required=True)
input_parser.add_suffix_mask(default="_mask")
input_parser.add_target_stack(required=False)
input_parser.add_alpha(default=0.01)
input_parser.add_verbose(default=0)
input_parser.add_gestational_age(required=False)
input_parser.add_prefix_output(default="")
input_parser.add_search_angle(default=180)
input_parser.add_multiresolution(default=0)
input_parser.add_log_script_execution(default=1)
input_parser.add_dir_input_templates(default=DIR_TEMPLATES)
input_parser.add_isotropic_resolution()
input_parser.add_reference()
input_parser.add_reference_mask()
input_parser.add_bias_field_correction(default=1)
input_parser.add_intensity_correction(default=1)
input_parser.add_iter_max(default=10)
input_parser.add_two_step_cycles(default=3)
input_parser.add_option(
option_string="--run-recon-subject-space",
type=int,
help="Turn on/off reconstruction in subject space",
default=1)
input_parser.add_option(
option_string="--run-recon-template-space",
type=int,
help="Turn on/off reconstruction in template space",
default=1)
input_parser.add_option(
option_string="--run-data-vs-simulated-data",
type=int,
help="Turn on/off comparison of data vs data simulated from the "
"obtained volumetric reconstruction",
default=1)
input_parser.add_outlier_rejection(default=0)
input_parser.add_use_robust_registration(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
# Write script execution call
if args.log_script_execution:
input_parser.write_performed_script_execution(
os.path.abspath(__file__))
dir_output_recon_subject_space = os.path.join(
args.dir_output, "recon_subject_space")
dir_output_recon_template_space = os.path.join(
args.dir_output, "recon_template_space")
dir_output_data_vs_simulatd_data = os.path.join(
args.dir_output, "data_vs_simulated_data")
# if args.run_recon_template_space and args.gestational_age is None:
# raise IOError("Gestational age must be set in order to pick the "
# "right template")
# get input stack names
files = os.listdir(args.dir_input)
input_files = []
mask_files = []
for file in files:
if (".nii" in file):
input_files.append("{0:}/{1:}".format(args.dir_input, file))
file_prefix = file[:-7] if (".nii.gz" in file) else file[:-4]
mask_name = "{0:}/{1:}.nii.gz".format(args.dir_mask, file_prefix)
if(not os.path.isfile(mask_name)):
mask_name = "{0:}/{1:}.nii".format(args.dir_mask, file_prefix)
assert(os.path.isfile(mask_name))
mask_files.append(mask_name)
if args.target_stack is None:
target_stack = input_files[0]
else:
target_stack = input_files
if args.run_recon_subject_space:
target_stack_index = input_files.index(target_stack)
cmd_args = []
cmd_args.append("--filenames %s" % (" ").join(input_files))
cmd_args.append("--filenames-masks %s" % (" ").join(mask_files))
cmd_args.append("--multiresolution %d" % args.multiresolution)
cmd_args.append("--target-stack-index %d" % target_stack_index)
cmd_args.append("--dir-output %s" % dir_output_recon_subject_space)
# cmd_args.append("--suffix-mask %s" % args.suffix_mask)
cmd_args.append("--intensity-correction %d" %
args.intensity_correction)
cmd_args.append("--alpha %s" % args.alpha)
cmd_args.append("--iter-max %d" % args.iter_max)
cmd_args.append("--two-step-cycles %d" % args.two_step_cycles)
cmd_args.append("--outlier-rejection %d" %
args.outlier_rejection)
cmd_args.append("--use-robust-registration %d" %
args.use_robust_registration)
cmd_args.append("--verbose %d" % args.verbose)
if args.isotropic_resolution is not None:
cmd_args.append("--isotropic-resolution %f" %
args.isotropic_resolution)
if args.reference is not None:
cmd_args.append("--reference %s" % args.reference)
if args.reference_mask is not None:
cmd_args.append("--reference-mask %s" % args.reference_mask)
cmd = "niftymic_reconstruct_volume %s" % (" ").join(cmd_args)
time_start_volrec = ph.start_timing()
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Reconstruction in subject space failed")
elapsed_time_volrec = ph.stop_timing(time_start_volrec)
else:
elapsed_time_volrec = ph.get_zero_time()
if args.run_recon_template_space:
# register recon to template space
pattern = "[a-zA-Z0-9_]+(stacks)[a-zA-Z0-9_]+(.nii.gz)"
p = re.compile(pattern)
reconstruction = [
os.path.join(
dir_output_recon_subject_space, p.match(f).group(0))
for f in os.listdir(dir_output_recon_subject_space)
if p.match(f)][0]
if('mask_manual' in args.dir_output):
# find the corresponding template by volume matching
reconstruction_mask = reconstruction
if(not ("_mask" in reconstruction)):
reconstruction_mask = ph.append_to_filename(reconstruction, "_mask")
template_stack_estimator = \
tse.TemplateStackEstimator.from_mask(
reconstruction_mask,
args.dir_input_templates)
template_mask = template_stack_estimator.get_path_to_template()
template = template_mask.replace('_mask_dil.nii.gz', '.nii.gz')
print('template name', template)
# template = os.path.join(
# args.dir_input_templates,
# "STA%d.nii.gz" % args.gestational_age)
# template_mask = os.path.join(
# args.dir_input_templates,
# "STA%d_mask.nii.gz" % args.gestational_age)
else:
template_folder = args.dir_output + "/../../mask_manual/reconstruct_outlier_gpr/"
file_names = os.listdir(template_folder)
template_names = [item for item in file_names if ("nii.gz" in item) and ("Masked" not in item)]
mask_names = [item for item in file_names if ("nii.gz" in item) and ("Masked" in item)]
template = os.path.join(template_folder, template_names[0])
template_mask = os.path.join(template_folder, mask_names[0])
cmd_args = []
cmd_args.append("--moving %s" % reconstruction)
cmd_args.append("--fixed %s" % template)
# if(use_spatiotemporal_template is False):
# cmd_args.append("--use-fixed-mask 1") # added by Guotai
# cmd_args.append("--template-mask %s" % template_mask) # micheal's code
cmd_args.append("--dir-input %s" % os.path.join(
dir_output_recon_subject_space,
"motion_correction"))
cmd_args.append("--dir-output %s" % dir_output_recon_template_space)
cmd_args.append("--suffix-mask %s" % args.suffix_mask)
cmd_args.append("--verbose %s" % args.verbose)
cmd = "niftymic_register_image %s" % (" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Registration to template space failed")
# reconstruct volume in template space
# pattern = "[a-zA-Z0-9_.]+(ResamplingToTemplateSpace.nii.gz)"
# p = re.compile(pattern)
# reconstruction_space = [
# os.path.join(dir_output_recon_template_space, p.match(f).group(0))
# for f in os.listdir(dir_output_recon_template_space)
# if p.match(f)][0]
dir_input = os.path.join(
dir_output_recon_template_space, "motion_correction")
cmd_args = []
cmd_args.append("--dir-input %s" % dir_input)
cmd_args.append("--dir-output %s" % dir_output_recon_template_space)
cmd_args.append("--reconstruction-space %s" % template)
cmd_args.append("--iter-max %d" % args.iter_max)
cmd_args.append("--alpha %s" % args.alpha)
cmd_args.append("--suffix-mask %s" % args.suffix_mask)
cmd = "niftymic_reconstruct_volume_from_slices %s" % \
(" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Reconstruction in template space failed")
pattern = "[a-zA-Z0-9_.]+(stacks[0-9]+).*(.nii.gz)"
p = re.compile(pattern)
reconstruction = {
p.match(f).group(1):
os.path.join(
dir_output_recon_template_space, p.match(f).group(0))
for f in os.listdir(dir_output_recon_template_space)
if p.match(f) and not p.match(f).group(0).endswith(
"ResamplingToTemplateSpace.nii.gz")}
key = reconstruction.keys()[0]
path_to_recon = reconstruction[key]
# Copy SRR to output directory
output = "%sSRR_%s_GW%d.nii.gz" % (
args.prefix_output, key, args.gestational_age)
path_to_output = os.path.join(args.dir_output, output)
cmd = "cp -p %s %s" % (path_to_recon, path_to_output)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Copy of SRR to output directory failed")
# Multiply template mask with reconstruction
cmd_args = []
cmd_args.append("--filename %s" % path_to_output)
cmd_args.append("--gestational-age %s" % args.gestational_age)
cmd_args.append("--verbose %s" % args.verbose)
cmd_args.append("--dir-input-templates %s " % args.dir_input_templates)
cmd = "niftymic_multiply_stack_with_mask %s" % (
" ").join(cmd_args)
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("SRR brain masking failed")
else:
elapsed_time_template = ph.get_zero_time()
if args.run_data_vs_simulated_data:
dir_input = os.path.join(
dir_output_recon_template_space, "motion_correction")
pattern = "[a-zA-Z0-9_.]+(stacks[0-9]+).*(.nii.gz)"
# pattern = "Masked_[a-zA-Z0-9_.]+(stacks[0-9]+).*(.nii.gz)"
p = re.compile(pattern)
reconstruction = {
p.match(f).group(1):
os.path.join(
dir_output_recon_template_space, p.match(f).group(0))
for f in os.listdir(dir_output_recon_template_space)
if p.match(f) and not p.match(f).group(0).endswith(
"ResamplingToTemplateSpace.nii.gz")}
key = reconstruction.keys()[0]
path_to_recon = reconstruction[key]
# Get simulated/projected slices
cmd_args = []
cmd_args.append("--dir-input %s" % dir_input)
cmd_args.append("--dir-output %s" % dir_output_data_vs_simulatd_data)
cmd_args.append("--reconstruction %s" % path_to_recon)
cmd_args.append("--copy-data 1")
cmd_args.append("--suffix-mask %s" % args.suffix_mask)
# cmd_args.append("--verbose %s" % args.verbose)
exe = os.path.abspath(simulate_stacks_from_reconstruction.__file__)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("SRR slice projections failed")
filenames_simulated = [
os.path.join(dir_output_data_vs_simulatd_data, os.path.basename(f))
for f in input_files]
dir_output_evaluation = os.path.join(
dir_output_data_vs_simulatd_data, "evaluation")
dir_output_figures = os.path.join(
dir_output_data_vs_simulatd_data, "figures")
dir_output_side_by_side = os.path.join(
dir_output_figures, "side-by-side")
# Evaluate slice similarities to ground truth
cmd_args = []
cmd_args.append("--filenames %s" % (" ").join(filenames_simulated))
cmd_args.append("--suffix-mask %s" % args.suffix_mask)
cmd_args.append("--measures NCC SSIM")
cmd_args.append("--dir-output %s" % dir_output_evaluation)
exe = os.path.abspath(evaluate_simulated_stack_similarity.__file__)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Evaluation of slice similarities failed")
# Generate figures showing the quantitative comparison
cmd_args = []
cmd_args.append("--dir-input %s" % dir_output_evaluation)
cmd_args.append("--dir-output %s" % dir_output_figures)
exe = os.path.abspath(
show_evaluated_simulated_stack_similarity.__file__)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
ph.print_warning("Visualization of slice similarities failed")
# Generate pdfs showing all the side-by-side comparisons
cmd_args = []
cmd_args.append("--filenames %s" % (" ").join(filenames_simulated))
cmd_args.append("--dir-output %s" % dir_output_side_by_side)
exe = os.path.abspath(
export_side_by_side_simulated_vs_original_slice_comparison.__file__)
cmd = "python %s %s" % (exe, (" ").join(cmd_args))
exit_code = ph.execute_command(cmd)
if exit_code != 0:
raise RuntimeError("Generation of PDF overview failed")
ph.print_title("Summary")
print("Computational Time for Volumetric Reconstruction: %s" %
elapsed_time_volrec)
print("Computational Time for Pipeline: %s" %
ph.stop_timing(time_start))
return 0
