Hi all,
Maybe someone here would know-
I came across a CT series, that include 2 series, saved as a single one.
When you scroll in axial slices, you see shoulder s to pelvis and then agin shoulders to pelvis in a later contrast.
Does anyone know if there is a tag in the DICOM header that can seperate them?
ITK-SNAP doesn’t recognize they are seperate, and also from viewing the header I could’nt find anything.
Suggestions?
Thanks!
If you are using itk.GDCMSeriesFileNames, you can try to use SetUseSeriesDetails(True) (link to doxygen documentation)
You can also try loading it in Slicer, to see if they are separated/loaded properly. Slicer uses ITK for image loading, with extra logic for better handling of DICOMs.
Thank you!
SimpleITK (which I use in python ) seems to not have SetUseSeriesDetails function…
@blowekamp: Do you have any idea why the flag mentioned in the discussion seems to not be exposed in SimpleITK? Any idea how to access it?
Thank you!
this is the error I got in Slicer:
Imported a DICOM directory, checking for extensions
Imported a DICOM directory, checking for extensions
Geometric issues were found with 1 of the series. Please use caution.
Warning in DICOM plugin Scalar Volume when examining loadable 2: Unnamed Series: Images are not equally spaced (a difference of -0.5 vs 1 in spacings was detected). If loaded image appears distorted, enable ‘Acquisition geometry regularization’ in Application settins / DICOM / DICOMScalarVolumePlugin. Please use caution.
Loading with imageIOName: GDCM
Window/level found in DICOM tags (center=40.0, width=350.0) has been applied to volume 2: Unnamed Series
Irregular volume geometry detected (maximum error of 218 mm is above tolerance threshold of 0.001 mm). Regularization transform is not added, as the option is disabled.
You may want to post this error message on the Slicer discourse here. They should be able to tell you if they use the flag I mentioned above or something similar.
Thanks! I will.
In SimpleITK the ImageSeriesReader_GetGDCMSeriesFileNames static member function has a useSeriesDetails argument which can be set to true.
You can find an example of the usage of the function here:
https://simpleitk.readthedocs.io/en/master/Examples/DicomSeriesReader/Documentation.html
Oh! it’s an argument Thanks!
Unfortunatly, it didn’t work.
I tried to run this code, but it generate an image with all slices, from 2 series:
(my dir= directory to the CT DICOM images)
reader = sitk.ImageSeriesReader()
dicom_names=sitk.ImageSeriesReader_GetGDCMSeriesFileNames(rmydir) ,useSeriesDetails=True)
reader.SetFileNames(dicom_names)
image = reader.Execute()
size = image.GetSize()
If anyone has another suggestions, it would be great.
@bsmarine maybe you could advise?
What is returned from ImageSeriesReader_GetGDCMSeriesIDs(rmydir)?
The GetCDCMSeriesFileNames method can also take a seriesID argument which may help separate the two.
This would be the standard way of getting access to a specific series when multiple series are in a directory. However, given that Slicer did not automatically detect this it likely will have some type of problem. You likely need to your data provenance such as how it has been created and modified as it may not be “valid DICOM”. Inspecting the DICOM tags to “hack” a split of the slices may work.
I would look at the variety of standalone converters that already exist, some of them are summarized here: NA-MIC Project Weeks | Website for NA-MIC Project Weeks.
Personally, I would recommend dcm2niix as the first tool to check out.
Another tool that I found very useful is https://github.com/pieper/dicomsort, you can use it to separate the content of your directly based on the values of defined tags.
Thanks! I will look into it
For now I only found that the only thing that might separate the 2 series is acquisition time and location in space.
I have had a similar problem with a series with 4 different acquistions. Separating by acquistion time seems to be the only way to split the series. I have done this using pydicom and dicom2nifti. Using this, I have separated the 4 different series which I can view independently on 3D slicer.
Also quite helpful is the new series_uid argument to itk.imread available in itk-5.4rc01.
I followed most of the suggestions above (useSeriesDetails , dcm2niix, Steve Pieper’s code etc.), but none of them worked for me. The issue is that if there are systemic problems with the way the DICOMs were acquired/saved, then it is very challenging to obtain the sub-components of each series. This has to do with legacy reasons (older scanners, different FOV saved as a “new” acquisition etc.).
Anyway, I wrote my own little script to read all the files for a given study, separate them into individual series and sub-series (based on acquisition number), and then dump them all to disk. It uses SimpleITK and pydicom, so adapt for your own needs. Trust but verify yourself.
import SimpleITK as sitk
import pydicom
def extract_series_and_dump(
dir_study_DICOM,
dir_save_NII
):
print('--' * 25)
print('idx:', row_idx)
print('dir_study_DICOM')
print(dir_study_DICOM)
print('dir_save_NII')
print(dir_save_NII)
## check if folder exists
if os.path.isdir(dir_study_DICOM) == True:
print('Processing study:')
print(dir_study_DICOM)
"""
First use GDCM to get a rough division of filenames for each series UID
"""
## get all series IDs in this study
l_series_IDs = sitk.ImageSeriesReader.GetGDCMSeriesIDs(dir_study_DICOM, useSeriesDetails=True)
if not l_series_IDs:
return False
num_series_found = len(l_series_IDs)
if num_series_found > 1:
print('num_series_found:', num_series_found)
"""
iterate + identify any sub-acquisitions in each series
"""
for idx in range(0, num_series_found):
series_UID = l_series_IDs[idx]
print('--' * 25)
print('idx:', idx)
print('seriesID:', series_UID)
## get all filenames for this series
series_file_names = sitk.ImageSeriesReader.GetGDCMSeriesFileNames(dir_study_DICOM, series_UID, useSeriesDetails=True)
"""
identify any sub-acquisitions in this series
----- use ACQUISITION NUMBER DICOM TAG -----
"""
d_series_by_acquisition_number = {}
for i, file_name in enumerate(series_file_names):
file_reader = sitk.ImageFileReader()
## set filename
file_reader.SetFileName(file_name)
## set flags
file_reader.LoadPrivateTagsOn()
## read file info
file_reader.ReadImageInformation()
## Get the list of metadata keys (DICOM tags)
meta_data_keys = file_reader.GetMetaDataKeys()
## get acquisition number
if "0020|0012" in meta_data_keys:
acquisition_number = file_reader.GetMetaData('0020|0012')
else:
continue
## get acquisition time
if "0008|0032" in meta_data_keys:
acquisition_time = file_reader.GetMetaData('0008|0032')
else:
acquisition_time = ''
## get series time
if "0008|0031" in meta_data_keys:
series_time = file_reader.GetMetaData('0008|0031')
else:
series_time = ''
## get location of slice
# instance_number = file_reader.GetMetaData("0020|0032").split("\\")[-1]
pydf = pydicom.dcmread(file_name)
## get pydicom slice location
if hasattr(pydf, 'SliceLocation'):
# print('SliceLocation:', pydf.SliceLocation)
instance_number = pydf.SliceLocation
else:
instance_number = -1
## protocol name
if "0018|103E" in meta_data_keys:
protocol_name = file_reader.GetMetaData("0018|103E")
else:
protocol_name = ''
## series description
if "0008|103E" in meta_data_keys:
series_description = file_reader.GetMetaData("0008|103E")
else:
series_description = ''
# print('acquisition_number:', acquisition_number, 'acquisition_time:', acquisition_time)
# print('series_time:', series_time)
# print('instance_number:', instance_number)
# print('protocol_name:', protocol_name)
# print('series_description:', series_description)
key_ = acquisition_number
if key_ not in d_series_by_acquisition_number:
d_series_by_acquisition_number[key_] = {}
## create
d_series_by_acquisition_number[key_]['files'] = []
d_series_by_acquisition_number[key_]['instances'] = []
## store
d_series_by_acquisition_number[key_]['files'].append(file_name)
d_series_by_acquisition_number[key_]['instances'].append(instance_number)
else:
d_series_by_acquisition_number[key_]['files'].append(file_name)
d_series_by_acquisition_number[key_]['instances'].append(instance_number)
## how many?
print('num_sub_series:', len(d_series_by_acquisition_number))
"""
Sort files in each sub-component by the position in volume
"""
d_sorted = {}
for key_ in d_series_by_acquisition_number:
## get
tfiles_ = d_series_by_acquisition_number[key_]['files']
tinstances_ = d_series_by_acquisition_number[key_]['instances']
## sort by position
sorted_files, sorted_instances = zip(*sorted(zip(tfiles_, tinstances_)))
## flip direction of files
sorted_files = sorted_files[::-1]
d_sorted[key_] = {}
## store
d_sorted[key_]['files'] = sorted_files
d_sorted[key_]['instances'] = sorted_instances
## how many?
print('num_sub_series:', len(d_sorted))
num_sub_series = len(d_sorted)
"""
Dump each sub-component + JSON to disk
"""
for tidx, key_ in enumerate(d_sorted):
l_sorted_files = d_sorted[key_]['files']
## initialize
series_reader = sitk.ImageSeriesReader()
## set
series_reader.SetFileNames(l_sorted_files)
## set flags
series_reader.MetaDataDictionaryArrayUpdateOn()
series_reader.LoadPrivateTagsOn()
## read
image_dicom = series_reader.Execute()
## Create the file reader and get the series_ID
file_reader = sitk.ImageFileReader()
## read file
temp_file_2_read = l_sorted_files[0]
# print(l_file_names[0])
## set filename
file_reader.SetFileName(temp_file_2_read)
## set flags
file_reader.LoadPrivateTagsOn()
## read file info
file_reader.ReadImageInformation()
## get metadata
metadata = {}
for key in file_reader.GetMetaDataKeys():
## get group + element
str_tag_group, str_tag_element = key.split('|')
## convert to HEX
hex_tag_group = hex(int(str_tag_group, 16))
hex_tag_element = hex(int(str_tag_element, 16))
## get keyword from tag using pydicom
tag = pydicom.tag.Tag(hex_tag_group, hex_tag_element)
keyword = pydicom.datadict.keyword_for_tag(tag)
## store
metadata[keyword] = file_reader.GetMetaData(key)
## get StudyID
temp_study_ID = file_reader.GetMetaData('0020|0010')
## get SeriesID
temp_series_ID = file_reader.GetMetaData('0020|0011')
## save image
if num_sub_series == 1:
## only one acquisition, save as is
fn_save = str(temp_study_ID).replace(" ", "") + '_' + str(temp_series_ID).replace(" ", "")
else:
## more than one acquisition, ID of this one
fn_save = str(temp_study_ID).replace(" ", "") + '_' + str(temp_series_ID).replace(" ", "") + '_acq' + str(tidx + 1)
## save
ffpn_save_image = os.path.join(dir_save_NII, fn_save + ".nii.gz")
print('study_series:')
print(fn_save)
## write
sitk.WriteImage(image_dicom, ffpn_save_image)
## save metadata
ffpn_save_metadata_json = os.path.join(dir_save_NII, fn_save + ".json")
## write metadata to disk
with open(ffpn_save_metadata_json, 'w') as f:
json.dump(metadata, f, indent=4) # indent for pretty printing
return True