Prerequisites

• Python 3.4+

• Dependencies:

  • pydicom

Installing pydicomext

pydicomext is currently available on PyPi. The simplest way to install is using pip at the command line:

pip install pydicomext

which installs the latest release. To install the latest code from the repository (usually stable, but may have undocumented changes or bugs):

pip install git+https://github.com/addisonElliott/pydicomext.git

For developers, you can clone the pydicomext repository and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:

git clone pip install git+https://github.com/addisonElliott/pydicomext.git
cd pydicomext
pip install .

or, for the last line, instead use:

pip install -e .

to install in ‘develop’ or ‘editable’ mode, where changes can be made to the local working code and Python will use the updated code.

Loading and displaying DICOM directory information

dicomDir = loadDirectory('DICOM DIRECTORY HERE')

print(dicomDir)

Result:

DicomDir
    Patient MF0307_V3
        Name: MF0307
        Issuer of ID:
        Birth Date: 19910613
        Birth Time: None
        Sex: F
        Other IDs:
        Other Names:
        Age: 026Y
        Size: 1.4732
        Weight: 79.3786
        Ethnic Group:
        Comments:
        Identity Removed: NO
        Position: HFS
        Studies:
            Study 1.3.12.2.1107.5.2.50.175614.30000017120515575743500000290
                Date: 20171206
                Time: 121132.400000
                Desc: CCIR-00900^CCIR-00956
                Series:
                    Series 1.3.12.2.1107.5.2.50.175614.30000017120614415244700000349
                        Date: 20171206
                        Time: 122855.475000
                        Desc: cine_trufi_cs_rt_adapt_trig_10sl_invf
                        Number: 10001
                        [18 datasets]
                    Series 1.3.12.2.1107.5.2.50.175614.30000017120614415244700000010
                        Date: 20171206
                        Time: 121807.861000
                        Desc: localizer_heart
                        Number: 1001
                        [14 datasets]
                    Series 1.3.12.2.1107.5.2.50.175614.30000017120614415244700000388
                        Date: 20171206
                        Time: 122855.577000
                        Desc: cine_trufi_cs_rt_adapt_trig_10sl_invf_INTP
                        Number: 11001
                        [25 datasets]
                    Series 1.3.12.2.1107.5.2.50.175614.30000017120614415244700000441
                        Date: 20171206
                        Time: 122903.466000
                        Desc: cine_trufi_cs_rt_adapt_trig_10sl_invf
                        Number: 12001
                        [18 datasets]
                    Series 1.3.12.2.1107.5.2.50.175614.30000017120614415244700000480
                        Date: 20171206
                        Time: 122903.570000
                        Desc: cine_trufi_cs_rt_adapt_trig_10sl_invf_INTP
                        Number: 13001
                        [25 datasets]
                    Series 1.3.12.2.1107.5.2.50.175614.30000017120614415244700000533
                        Date: 20171206
                        Time: 122911.695000
                        Desc: cine_trufi_cs_rt_adapt_trig_10sl_invf
                        Number: 14001
                        [20 datasets]
                    Series 1.3.12.2.1107.5.2.50.175614.30000017120614415244700000576
                        Date: 20171206
                        Time: 122911.796000
                        Desc: cine_trufi_cs_rt_adapt_trig_10sl_invf_INTP
                        Number: 15001
                        [25 datasets]
                    Series 1.3.12.2.1107.5.2.50.175614.30000017120614415244700000629
                        Date: 20171206
                        Time: 122919.754000
                        Desc: cine_trufi_cs_rt_adapt_trig_10sl_invf
                        Number: 16001
                        [19 datasets]
                    Series 1.3.12.2.1107.5.2.50.175614.30000017120614415244700000670
                        Date: 20171206
                        Time: 122919.862000
                        Desc: cine_trufi_cs_rt_adapt_trig_10sl_invf_INTP
                        Number: 17001
                        [25 datasets]
                    ...

Combining cMRI scans into a volume

dicomDir = loadDirectory('DICOM DIRECTORY HERE')

# Retrieves the only patient from the directory, throws error if more than one patient
patient = dicomDir.only()

# Retrieves the only study from the patient, throws error if more than one study
study = patient.only()

# Retrieve a list of all series that have the description 'cine_trufi_cs_2_shot'
# Each series is a class pydicomext.Series
# This DICOM directory has multiple series that represent a Z-slice of the heart
# Each series has multiple temporal frames of that slice of the heart at a certain time frame
seriess = list(iter(filter(lambda x: x.description == 'cine_trufi_cs_2_shot', study.values())))

# Merge the series into one, essentially takes datasets from each series and puts into one big series
series = mergeSeries(seriess)

# Combine the series into a Numpy volume
volume = series.combine(methods=[MethodType.StackPosition, MethodType.TemporalPositionIndex])

# Print data of the volume, which is of type pydicomext.Volume
# Can access Numpy array by volume.data
print(volume)

Result:

Volume
    Space: left-posterior-superior
    Orientation: [[-5.80474000e-01  4.44949000e-01 -6.81959360e-01]
        [ 2.95683000e-07  8.37502000e-01  5.46433310e-01]
        [-8.14278000e-01 -3.17191000e-01  4.86148268e-01]]
    Origin: [  30.0193 -150.763   271.145 ]
    Spacing: [1.      1.      1.47266 1.47266]
    Volume shape: (12, 16, 256, 256)