This is a brief overview of how to use the dcmstack Python package. For details refer to API Documentation.
If you have an aquisition that you would like to turn into a single DicomStack object then you may want to do this directly.
>>> import dcmstack, dicom >>> from glob import glob >>> src_paths = glob('032-MPRAGEAXTI900Pre/*.dcm') >>> my_stack = dcmstack.DicomStack() >>> for src_path in src_paths: ... src_dcm = dicom.read_file(src_path) ... my_stack.add_dcm(src_dcm)
If you are unsure how many stacks you want from a collection of DICOM data sets then you should use the parse_and_stack function. This will group together data sets from the same DICOM series.
>>> import dcmstack >>> from glob import glob >>> src_paths = glob('dicom_data/*.dcm') >>> stacks = dcmstack.parse_and_stack(src_paths)
Any keyword arguments for the DicomStack constructor can also be passed to parse_and_stack.
By default, if there is more than one 3D volume in the stack the software will try to guess the meta key to sort the fourth (time) dimension. To specify the meta data key for the fourth dimension or stack along the fifth (vector) dimension, use the time_order and vector_order arguments to the DicomStack constructor.
The parse_and_stack function groups data sets using a tuple of meta data keys provided as the argument group_by. The default values should group datasets from the same series into the same stack. The result is a dictionary where the keys are the matching tuples of meta data values, and the values are the are the corresponding stacks.
Once you have created your DicomStack objects you will typically want to get the array of voxel data, get the affine transform, or create a Nifti1Image.
>>> stack_data = my_stack.get_data() >>> stack_affine = my_stack.get_affine() >>> nii = my_stack.to_nifti()
The meta data from the source DICOM data sets can be summarized into a DcmMetaExtension which is embeded into the Nifti header. To do this you can either pass True for the embed_meta parameter to DicomStack.to_nifti or you can immediately get a NiftiWrapper with DicomStack.to_nifti_wrapper.
By default the meta data is filtered to reduce the chance of including private health information. This filtering can be controlled with the meta_filter parameter to the DicomStack constructor.
IT IS YOUR RESPONSABILITY TO KNOW IF THERE IS PRIVATE HEALTH INFORMATION IN THE RESULTING FILE AND TREAT SUCH FILES APPROPRIATELY.
The NiftiWrapper class can be used to work with extended Nifti files. It wraps a Nifti1Image from the nibabel package. As mentioned above, these can be created directly from a DicomStack.
>>> import dcmstack, dicom >>> from glob import glob >>> src_paths = glob('032-MPRAGEAXTI900Pre/*.dcm') >>> my_stack = dcmstack.DicomStack() >>> for src_path in src_paths: ... src_dcm = dicom.read_file(src_path) ... my_stack.add_dcm(src_dcm) ... >>> nii_wrp = my_stack.to_nifti_wrapper() >>> nii_wrp.get_meta('InversionTime') 900.0
They can also be created by passing a Nifti1Image to the NiftiWrapper constructor or by passing the path to a Nifti file to NiftiWrapper.from_filename.
The NiftiWrapper objects have attribute nii_img pointing to the Nifti1Image being wrapped and the attribute meta_ext pointing to the DcmMetaExtension. There are also a number of methods for working with the image data and meta data together. For example merging or splitting the data set along the time axis.
Meta data that is constant can be accessed with dict-style lookups. The more general access method is get_meta which can optionally take an index into the voxel array in order to provide access to varying meta data.
>>> nii_wrp = NiftiWrapper.from_filename('032-MPRAGEAXTI900Pre.nii.gz') >>> nii_wrp['InversionTime'] 900.0 >>> nii_wrp.get_meta('InversionTime') 900.0 >>> nii_wrp['InstanceNumber'] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "build/bdist.linux-x86_64/egg/dcmstack/dcmmeta.py", line 1026, in __getitem__ KeyError: 'InstanceNumber' >>> nii_wrp.get_meta('InstanceNumber') >>> nii_wrp.get_meta('InstanceNumber', index=(0,0,0)) 1 >>> nii_wrp.get_meta('InstanceNumber', index=(0,0,1)) 2
We can create a NiftiWrapper by merging a sequence of NiftiWrapper objects using the class method from_sequence. Conversely, we can split a NiftiWrapper into a sequence if NiftiWrapper objects using the method split.
>>> from dcmstack.dcmmeta import NiftiWrapper >>> nw1 = NiftiWrapper.from_filename('img1.nii.gz') >>> nw2 = NiftiWrapper.from_filename('img2.nii.gz') >>> print nw1.nii_img.get_shape() (384, 512, 60) >>> print nw2.nii_img.get_shape() (384, 512, 60) >>> print nw1.get_meta('EchoTime') 11.0 >>> print nw2.get_meta('EchoTime') 87.0 >>> merged = NiftiWrapper.from_sequence([nw1, nw2]) >>> print merged.nii_img.get_shape() (384, 512, 60, 2) >>> print merged.get_meta('EchoTime', index=(0,0,0,0) 11.0 >>> print merged.get_meta('EchoTime', index=(0,0,0,1) 87.0 >>> splits = list(merge.split()) >>> print splits.nii_img.get_shape() (384, 512, 60) >>> print splits.nii_img.get_shape() (384, 512, 60) >>> print splits.get_meta('EchoTime') 11.0 >>> print splits.get_meta('EchoTime') 87.0
It is generally recommended that meta data is accessed through the NiftiWrapper class since it can do some checks between the meta data and the image data. For example, it will make sure the dimensions and slice direction have not changed before using varying meta data.
However certain actions are much easier when accessing the meta data extension directly.
>>> from dcmstack.dcmmeta import NiftiWrapper >>> nw1 = NiftiWrapper.from_filename('img.nii.gz') >>> nw.meta_ext.shape >>> (384, 512, 60, 2) >>> print nw.meta_ext.get_values('EchoTime') [11.0, 87.0] >>> print nw.meta_ext.get_classification('EchoTime') ('time', 'samples')