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dcmstack.py
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dcmstack.py
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# -*- coding: utf-8 -*-
"""dcmstack allows series of DICOM images to be stacked into multi-dimensional arrays."""
import os
from os import path as op
import string
import errno
from glob import glob
import nibabel as nb
import imghdr
from .base import (
TraitedSpec,
DynamicTraitedSpec,
InputMultiPath,
File,
Directory,
traits,
BaseInterface,
isdefined,
Undefined,
)
have_dcmstack = True
try:
import dicom
import dcmstack
from dcmstack.dcmmeta import NiftiWrapper
except ImportError:
have_dcmstack = False
def sanitize_path_comp(path_comp):
result = []
for char in path_comp:
if char not in string.letters + string.digits + "-_.":
result.append("_")
else:
result.append(char)
return "".join(result)
class NiftiGeneratorBaseInputSpec(TraitedSpec):
out_format = traits.Str(
desc="String which can be formatted with "
"meta data to create the output filename(s)"
)
out_ext = traits.Str(".nii.gz", usedefault=True, desc="Determines output file type")
out_path = Directory(desc="output path, current working directory if not set")
class NiftiGeneratorBase(BaseInterface):
"""Base class for interfaces that produce Nifti files, potentially with
embedded meta data."""
def _get_out_path(self, meta, idx=None):
"""Return the output path for the gernerated Nifti."""
if self.inputs.out_format:
out_fmt = self.inputs.out_format
else:
# If no out_format is specified, use a sane default that will work
# with the provided meta data.
out_fmt = []
if idx is not None:
out_fmt.append("%03d" % idx)
if "SeriesNumber" in meta:
out_fmt.append("%(SeriesNumber)03d")
if "ProtocolName" in meta:
out_fmt.append("%(ProtocolName)s")
elif "SeriesDescription" in meta:
out_fmt.append("%(SeriesDescription)s")
else:
out_fmt.append("sequence")
out_fmt = "-".join(out_fmt)
out_fn = (out_fmt % meta) + self.inputs.out_ext
out_fn = sanitize_path_comp(out_fn)
out_path = os.getcwd()
if isdefined(self.inputs.out_path):
out_path = op.abspath(self.inputs.out_path)
# now, mkdir -p $out_path
try:
os.makedirs(out_path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and op.isdir(out_path):
pass
else:
raise
return op.join(out_path, out_fn)
class DcmStackInputSpec(NiftiGeneratorBaseInputSpec):
dicom_files = traits.Either(
InputMultiPath(File(exists=True)),
Directory(exists=True),
traits.Str(),
mandatory=True,
)
embed_meta = traits.Bool(desc="Embed DICOM meta data into result")
exclude_regexes = traits.List(
desc="Meta data to exclude, suplementing " "any default exclude filters"
)
include_regexes = traits.List(
desc="Meta data to include, overriding any " "exclude filters"
)
force_read = traits.Bool(
True, usedefault=True, desc=("Force reading files without DICM marker")
)
class DcmStackOutputSpec(TraitedSpec):
out_file = File(exists=True)
class DcmStack(NiftiGeneratorBase):
"""Create one Nifti file from a set of DICOM files. Can optionally embed
meta data.
Example
-------
>>> from nipype.interfaces.dcmstack import DcmStack
>>> stacker = DcmStack()
>>> stacker.inputs.dicom_files = 'path/to/series/'
>>> stacker.run() # doctest: +SKIP
>>> result.outputs.out_file # doctest: +SKIP
'/path/to/cwd/sequence.nii.gz'
"""
input_spec = DcmStackInputSpec
output_spec = DcmStackOutputSpec
def _get_filelist(self, trait_input):
if isinstance(trait_input, (str, bytes)):
if op.isdir(trait_input):
return glob(op.join(trait_input, "*.dcm"))
else:
return glob(trait_input)
return trait_input
def _run_interface(self, runtime):
src_paths = self._get_filelist(self.inputs.dicom_files)
include_regexes = dcmstack.default_key_incl_res
if isdefined(self.inputs.include_regexes):
include_regexes += self.inputs.include_regexes
exclude_regexes = dcmstack.default_key_excl_res
if isdefined(self.inputs.exclude_regexes):
exclude_regexes += self.inputs.exclude_regexes
meta_filter = dcmstack.make_key_regex_filter(exclude_regexes, include_regexes)
stack = dcmstack.DicomStack(meta_filter=meta_filter)
for src_path in src_paths:
if not imghdr.what(src_path) == "gif":
src_dcm = dicom.read_file(src_path, force=self.inputs.force_read)
stack.add_dcm(src_dcm)
nii = stack.to_nifti(embed_meta=True)
nw = NiftiWrapper(nii)
self.out_path = self._get_out_path(
nw.meta_ext.get_class_dict(("global", "const"))
)
if not self.inputs.embed_meta:
nw.remove_extension()
nb.save(nii, self.out_path)
return runtime
def _list_outputs(self):
outputs = self._outputs().get()
outputs["out_file"] = self.out_path
return outputs
class GroupAndStackOutputSpec(TraitedSpec):
out_list = traits.List(desc="List of output nifti files")
class GroupAndStack(DcmStack):
"""Create (potentially) multiple Nifti files for a set of DICOM files."""
input_spec = DcmStackInputSpec
output_spec = GroupAndStackOutputSpec
def _run_interface(self, runtime):
src_paths = self._get_filelist(self.inputs.dicom_files)
stacks = dcmstack.parse_and_stack(src_paths)
self.out_list = []
for key, stack in list(stacks.items()):
nw = NiftiWrapper(stack.to_nifti(embed_meta=True))
const_meta = nw.meta_ext.get_class_dict(("global", "const"))
out_path = self._get_out_path(const_meta)
if not self.inputs.embed_meta:
nw.remove_extension()
nb.save(nw.nii_img, out_path)
self.out_list.append(out_path)
return runtime
def _list_outputs(self):
outputs = self._outputs().get()
outputs["out_list"] = self.out_list
return outputs
class LookupMetaInputSpec(TraitedSpec):
in_file = File(mandatory=True, exists=True, desc="The input Nifti file")
meta_keys = traits.Either(
traits.List(),
traits.Dict(),
mandatory=True,
desc=(
"List of meta data keys to lookup, or a "
"dict where keys specify the meta data "
"keys to lookup and the values specify "
"the output names"
),
)
class LookupMeta(BaseInterface):
"""Lookup meta data values from a Nifti with embedded meta data.
Example
-------
>>> from nipype.interfaces import dcmstack
>>> lookup = dcmstack.LookupMeta()
>>> lookup.inputs.in_file = 'functional.nii'
>>> lookup.inputs.meta_keys = {'RepetitionTime' : 'TR', \
'EchoTime' : 'TE'}
>>> result = lookup.run() # doctest: +SKIP
>>> result.outputs.TR # doctest: +SKIP
9500.0
>>> result.outputs.TE # doctest: +SKIP
95.0
"""
input_spec = LookupMetaInputSpec
output_spec = DynamicTraitedSpec
def _make_name_map(self):
if isinstance(self.inputs.meta_keys, list):
self._meta_keys = {}
for key in self.inputs.meta_keys:
self._meta_keys[key] = key
else:
self._meta_keys = self.inputs.meta_keys
def _outputs(self):
self._make_name_map()
outputs = super(LookupMeta, self)._outputs()
undefined_traits = {}
for out_name in list(self._meta_keys.values()):
outputs.add_trait(out_name, traits.Any)
undefined_traits[out_name] = Undefined
outputs.trait_set(trait_change_notify=False, **undefined_traits)
# Not sure why this is needed
for out_name in list(self._meta_keys.values()):
_ = getattr(outputs, out_name)
return outputs
def _run_interface(self, runtime):
# If the 'meta_keys' input is a list, covert it to a dict
self._make_name_map()
nw = NiftiWrapper.from_filename(self.inputs.in_file)
self.result = {}
for meta_key, out_name in list(self._meta_keys.items()):
self.result[out_name] = nw.meta_ext.get_values(meta_key)
return runtime
def _list_outputs(self):
outputs = self._outputs().get()
outputs.update(self.result)
return outputs
class CopyMetaInputSpec(TraitedSpec):
src_file = File(mandatory=True, exists=True)
dest_file = File(mandatory=True, exists=True)
include_classes = traits.List(
desc="List of specific meta data "
"classifications to include. If not "
"specified include everything."
)
exclude_classes = traits.List(
desc="List of meta data " "classifications to exclude"
)
class CopyMetaOutputSpec(TraitedSpec):
dest_file = File(exists=True)
class CopyMeta(BaseInterface):
"""Copy meta data from one Nifti file to another. Useful for preserving
meta data after some processing steps."""
input_spec = CopyMetaInputSpec
output_spec = CopyMetaOutputSpec
def _run_interface(self, runtime):
src_nii = nb.load(self.inputs.src_file)
src = NiftiWrapper(src_nii, make_empty=True)
dest_nii = nb.load(self.inputs.dest_file)
dest = NiftiWrapper(dest_nii, make_empty=True)
classes = src.meta_ext.get_valid_classes()
if self.inputs.include_classes:
classes = [cls for cls in classes if cls in self.inputs.include_classes]
if self.inputs.exclude_classes:
classes = [cls for cls in classes if cls not in self.inputs.exclude_classes]
for cls in classes:
src_dict = src.meta_ext.get_class_dict(cls)
dest_dict = dest.meta_ext.get_class_dict(cls)
dest_dict.update(src_dict)
# Update the shape and slice dimension to reflect the meta extension
# update.
dest.meta_ext.slice_dim = src.meta_ext.slice_dim
dest.meta_ext.shape = src.meta_ext.shape
self.out_path = op.join(os.getcwd(), op.basename(self.inputs.dest_file))
dest.to_filename(self.out_path)
return runtime
def _list_outputs(self):
outputs = self._outputs().get()
outputs["dest_file"] = self.out_path
return outputs
class MergeNiftiInputSpec(NiftiGeneratorBaseInputSpec):
in_files = traits.List(mandatory=True, desc="List of Nifti files to merge")
sort_order = traits.Either(
traits.Str(),
traits.List(),
desc="One or more meta data keys to " "sort files by.",
)
merge_dim = traits.Int(
desc="Dimension to merge along. If not "
"specified, the last singular or "
"non-existant dimension is used."
)
class MergeNiftiOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="Merged Nifti file")
def make_key_func(meta_keys, index=None):
def key_func(src_nii):
result = [src_nii.get_meta(key, index) for key in meta_keys]
return result
return key_func
class MergeNifti(NiftiGeneratorBase):
"""Merge multiple Nifti files into one. Merges together meta data
extensions as well."""
input_spec = MergeNiftiInputSpec
output_spec = MergeNiftiOutputSpec
def _run_interface(self, runtime):
niis = [nb.load(fn) for fn in self.inputs.in_files]
nws = [NiftiWrapper(nii, make_empty=True) for nii in niis]
if self.inputs.sort_order:
sort_order = self.inputs.sort_order
if isinstance(sort_order, (str, bytes)):
sort_order = [sort_order]
nws.sort(key=make_key_func(sort_order))
if self.inputs.merge_dim == traits.Undefined:
merge_dim = None
else:
merge_dim = self.inputs.merge_dim
merged = NiftiWrapper.from_sequence(nws, merge_dim)
const_meta = merged.meta_ext.get_class_dict(("global", "const"))
self.out_path = self._get_out_path(const_meta)
nb.save(merged.nii_img, self.out_path)
return runtime
def _list_outputs(self):
outputs = self._outputs().get()
outputs["out_file"] = self.out_path
return outputs
class SplitNiftiInputSpec(NiftiGeneratorBaseInputSpec):
in_file = File(exists=True, mandatory=True, desc="Nifti file to split")
split_dim = traits.Int(
desc="Dimension to split along. If not "
"specified, the last dimension is used."
)
class SplitNiftiOutputSpec(TraitedSpec):
out_list = traits.List(File(exists=True), desc="Split Nifti files")
class SplitNifti(NiftiGeneratorBase):
"""
Split one Nifti file into many along the specified dimension. Each
result has an updated meta data extension as well.
"""
input_spec = SplitNiftiInputSpec
output_spec = SplitNiftiOutputSpec
def _run_interface(self, runtime):
self.out_list = []
nii = nb.load(self.inputs.in_file)
nw = NiftiWrapper(nii, make_empty=True)
split_dim = None
if self.inputs.split_dim == traits.Undefined:
split_dim = None
else:
split_dim = self.inputs.split_dim
for split_idx, split_nw in enumerate(nw.split(split_dim)):
const_meta = split_nw.meta_ext.get_class_dict(("global", "const"))
out_path = self._get_out_path(const_meta, idx=split_idx)
nb.save(split_nw.nii_img, out_path)
self.out_list.append(out_path)
return runtime
def _list_outputs(self):
outputs = self._outputs().get()
outputs["out_list"] = self.out_list
return outputs