dicom2nifti

Python library for converting dicom files to nifti

Using dicom2nifti

Installation

conda:

conda install -c conda-forge dicom2nifti

pip:

pip install dicom2nifti

To support compressed dicom formats (JPEG, JPEG2000) we use GDCM in combination with pydicom. Please install gdcm as required. https://pydicom.github.io/pydicom/stable/tutorials/installation.html

Updating

conda:

conda update dicom2nifti

pip:

pip install dicom2nifti --upgrade

Usage

Command line

dicom2nifti [-h] [-G] [-r] [-o RESAMPLE_ORDER] [-p RESAMPLE_PADDING] [-M] [-C] [-R] input_directory output_directory

for more information

dicom2nifti -h

From python

Converting a directory with dicom files to nifti files

import dicom2nifti

dicom2nifti.convert_directory(dicom_directory, output_folder, compression=True, reorient=True)

Converting a directory with only 1 series to 1 nifti file

import dicom2nifti

dicom2nifti.dicom_series_to_nifti(original_dicom_directory, output_file, reorient_nifti=True)

Supported data

Most anatomical data for CT and MR should be supported as long as they are in classical dicom files.

Try avoiding "Implicit VR Endian" if possible as this makes converting non anatomical (i.e. DTI, fMRI, ...) much more difficult.

There is some vendor specific support, more specifically for 4D imaging like fMRI and DTI/DKI

Gantry tilted CT

By default support for gantry tilted ct is disabled as we validate image orthogonality. You can explicitly allow gantry tilted data by disabling this validation.

Standard this will result in a nifti file where the gantry tilt is captured by the affine matrix. We also provide the option to resample the data to an orthogonal nifti. For this resampling we use scipy.ndimage.interpolation.affine_transform. You should configure the padding value and spline interpolation order. IMPORTANT NOTE: When using the orthogonal resampling the output nifti will always be reoriented

Command line:

dicom2nifti -G -r -o 1 -p -1000 input_directory output_directory

Python code:

import dicom2nifti
import dicom2nifti.settings as settings

settings.disable_validate_orthogonal()
settings.enable_resampling()
settings.set_resample_spline_interpolation_order(1)
settings.set_resample_padding(-1000)

dicom2nifti.convert_directory(dicom_directory, output_folder)

Inconsistent slice incremement

By default support for inconsistent slice increments is disabled. You can explicitly allow the images but should also use resampling (similar to the gantry tilted support) to avoid geometric distortions due to the inconsistent slice increments. You should configure the padding value and spline interpolation order

Command line:

dicom2nifti -I -r -o 1 -p -1000 input_directory output_directory

Python code:

import dicom2nifti
import dicom2nifti.settings as settings

settings.disable_validate_slice_increment()
settings.enable_resampling()
settings.set_resample_spline_interpolation_order(1)
settings.set_resample_padding(-1000)

dicom2nifti.convert_directory(dicom_directory, output_folder)

Single slice

By default we do not convert single slice images. You can explicitly allow the conversion of single slices.

Command line:

dicom2nifti -S input_directory output_directory

Python code:

import dicom2nifti
import dicom2nifti.settings as settings

settings.disable_validate_slicecount()

dicom2nifti.convert_directory(dicom_directory, output_folder)

GE MR

Anatomical data should all be support. 4D images like fMRI and DTI/DKI are supported.

Siemens MR

Anatomical data should all be support. 4D images like fMRI and DTI/DKI are supported.

Philips MR

For classic dicom files dicom2nifti support anatomical. For classic dicom files 4D images like fMRI and DTI/DKI are supported.

For "Philips Enhanced Dicom" there is no support for "Implicit VR Endian" transfer syntax. For the others we support anatomical and 4D images like fMRI and DTI/DKI.

Hitachi MR

Anatomical data should all be support. 4D images like fMRI and DTI/DKI are NOT supported. Anyone willing to share DTI and/or fMRI dicom form Hitachi scanners please contact us.

Unsupported data

If you encounter unsupported data you can help the development of dicom2nifti by providing a dataset. This dataset should be anonymised (but leave as much of the private fields as possible).