def main():
input_parser = InputArgparser(description="Convert NIfTI to DICOM image", )
input_parser.add_filename(required=True)
input_parser.add_option(
option_string="--template",
type=str,
required=True,
help="Template DICOM to extract relevant DICOM tags.",
)
input_parser.add_dir_output(required=True)
input_parser.add_label(
help="Label used for series description of DICOM output.",
default="SRR_NiftyMIC")
input_parser.add_argument(
"--volume",
"-volume",
action='store_true',
help="If given, the output DICOM file is combined as 3D volume")
args = input_parser.parse_args()
input_parser.print_arguments(args)
# Prepare for final DICOM output
ph.create_directory(args.dir_output)
if args.volume:
dir_output_2d_slices = os.path.join(DIR_TMP, "dicom_slices")
else:
dir_output_2d_slices = os.path.join(args.dir_output, args.label)
ph.create_directory(dir_output_2d_slices, delete_files=True)
# read NiftyMIC version (if available)
data_reader = dr.ImageHeaderReader(args.filename)
data_reader.read_data()
niftymic_version = data_reader.get_niftymic_version()
if niftymic_version is None:
niftymic_version = "NiftyMIC"
else:
niftymic_version = "NiftyMIC-v%s" % niftymic_version
# Create set of 2D DICOM slices from 3D NIfTI image
# (correct image orientation!)
ph.print_title("Create set of 2D DICOM slices from 3D NIfTI image")
cmd_args = ["nifti2dicom"]
cmd_args.append("-i '%s'" % args.filename)
cmd_args.append("-o '%s'" % dir_output_2d_slices)
cmd_args.append("-d '%s'" % args.template)
cmd_args.append("--prefix ''")
cmd_args.append("--seriesdescription '%s'" % args.label)
cmd_args.append("--accessionnumber '%s'" % ACCESSION_NUMBER)
cmd_args.append("--seriesnumber '%s'" % SERIES_NUMBER)
cmd_args.append("--institutionname '%s'" % IMAGE_COMMENTS)
# Overwrite default "nifti2dicom" tags which would be added otherwise
# (no deletion/update with empty '' sufficient to overwrite them)
cmd_args.append("--manufacturersmodelname '%s'" % "NiftyMIC")
cmd_args.append("--protocolname '%s'" % niftymic_version)
cmd_args.append("-y")
ph.execute_command(" ".join(cmd_args))
if args.volume:
path_to_output = os.path.join(args.dir_output, "%s.dcm" % args.label)
# Combine set of 2D DICOM slices to form 3D DICOM image
# (image orientation stays correct)
ph.print_title("Combine set of 2D DICOM slices to form 3D DICOM image")
cmd_args = ["medcon"]
cmd_args.append("-f '%s'/*.dcm" % dir_output_2d_slices)
cmd_args.append("-o '%s'" % path_to_output)
cmd_args.append("-c dicom")
cmd_args.append("-stack3d")
cmd_args.append("-n")
cmd_args.append("-qc")
cmd_args.append("-w")
ph.execute_command(" ".join(cmd_args))
# Update all relevant DICOM tags accordingly
ph.print_title("Update all relevant DICOM tags accordingly")
print("")
dataset_template = pydicom.dcmread(args.template)
dataset = pydicom.dcmread(path_to_output)
# Copy tags from template (to guarantee grouping with original data)
update_dicom_tags = {}
for tag in COPY_DICOM_TAGS:
try:
update_dicom_tags[tag] = getattr(dataset_template, tag)
except:
update_dicom_tags[tag] = ""
# Additional tags
update_dicom_tags["SeriesDescription"] = args.label
update_dicom_tags["InstitutionName"] = institution_name
update_dicom_tags["ImageComments"] = IMAGE_COMMENTS
update_dicom_tags["AccessionNumber"] = ACCESSION_NUMBER
update_dicom_tags["SeriesNumber"] = SERIES_NUMBER
for tag in sorted(update_dicom_tags.keys()):
value = update_dicom_tags[tag]
setattr(dataset, tag, value)
ph.print_info("%s: '%s'" % (tag, value))
dataset.save_as(path_to_output)
print("")
ph.print_info("3D DICOM image written to '%s'" % path_to_output)
else:
ph.print_info("DICOM images written to '%s'" % dir_output_2d_slices)
return 0
def main():
time_start = ph.start_timing()
np.set_printoptions(precision=3)
input_parser = InputArgparser(
description="Perform Bias Field correction using N4ITK.", )
input_parser.add_filename(required=True)
input_parser.add_output(required=True)
input_parser.add_filename_mask()
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_config(default=1)
input_parser.add_verbose(default=0)
args = input_parser.parse_args()
input_parser.print_arguments(args)
if np.alltrue([not args.output.endswith(t) for t in ALLOWED_EXTENSIONS]):
raise ValueError(
"output filename invalid; allowed extensions are: %s" %
", ".join(ALLOWED_EXTENSIONS))
if args.log_config:
input_parser.log_config(os.path.abspath(__file__))
# Read data
stack = st.Stack.from_filename(
file_path=args.filename,
file_path_mask=args.filename_mask,
extract_slices=False,
)
# Perform Bias Field Correction
# ph.print_title("Perform Bias Field Correction")
bias_field_corrector = n4itk.N4BiasFieldCorrection(
stack=stack,
use_mask=True if args.filename_mask is not None else False,
convergence_threshold=args.convergence_threshold,
spline_order=args.spline_order,
wiener_filter_noise=args.wiener_filter_noise,
bias_field_fwhm=args.bias_field_fwhm,
)
ph.print_info("N4ITK Bias Field Correction ... ", newline=False)
bias_field_corrector.run_bias_field_correction()
stack_corrected = bias_field_corrector.get_bias_field_corrected_stack()
print("done")
dw.DataWriter.write_image(stack_corrected.sitk, args.output)
elapsed_time = ph.stop_timing(time_start)
if args.verbose:
ph.show_niftis([args.filename, args.output])
ph.print_title("Summary")
exe_file_info = os.path.basename(os.path.abspath(__file__)).split(".")[0]
print("%s | Computational Time for Bias Field Correction: %s" %
(exe_file_info, elapsed_time))
return 0
