Quickstart

The most basic usage case is reading a single DICOM image (.dcm file) as an Image instance.

from dicom_parser import Image

# Create a DICOM Image object
image = Image('/path/to/dicom/file.dcm')

Coversion to Python’s native types

dicom_parser provides dict-like access to the parsed values of the header’s data-elements. The raw values as read by pydicom remain accessible through the raw attribute.

Examples

Decimal String (DS) to float using the Header class’s get() method:

raw_value = image.header.raw['ImagingFrequency'].value
raw_value
>> "123.25993"
type(raw_value)
>> str

parsed_value = image.header.get('ImagingFrequency')
parsed_value
>> 123.25993
type(parsed_value)
>> float

Age String (AS) to float:

raw_value = image.header.raw['PatientAge'].value
raw_value
>> "027Y"
type(raw_value)
>> str

parsed_value = image.header.get('PatientAge')
parsed_value
>> 27.0
type(parsed_value)
>> float

Date String (DA) to datetime.date using the Header class’s indexing operator/subscript notation:

raw_value = image.header.raw['PatientBirthDate'].value
raw_value
>> "19901214"
type(raw_value)
>> str

parsed_value = image.header['PatientBirthDate']
parsed_value
>> datetime.date(1990, 12, 14)
type(parsed_value)
>> datetime.date

Code String (CS) to a verbose value or set of values:

raw_value = image.header.raw['SequenceVariant'].value
raw_value
>> ['SP', 'OSP']
type(raw_value)
>> pydicom.multival.MultiValue

parsed_value = image.header['SequenceVariant']
parsed_value
>> {'Oversampling Phase', 'Spoiled'}
type(parsed_value)
>> set

Et cetera.

Note

The dict-like functionality also includes safe getting:

image.header.get('MissingKey')
>> None

image.header.get('MissingKey', 'DefaultValue')
>> 'DefaultValue'

As well as raising a KeyError for missing keys with the indexing operator:

image.header['MissingKey']
>> ...
>> KeyError: "The keyword: 'MissingKey' does not exist in the header!"

Read DICOM series directory as a Series

Another useful class this package offers is the Series class:

from dicom_parser import Series

series = Series('/some/dicom/series/')

The Series instance allows us to easily query the underlying images’ headers using its get() method:

# Single value
series.get('EchoTime')
>> 3.04

# Multiple values
series.get('InstanceNumber')
>> [1, 2, 3]

# No value
series.get('MissingKey')
>> None

# Default value
series.get('MissingKey', 'default_value')
>> 'default_value'

Similarly to the Image class, we can also use the indexing operator:

# Single value
series['RepetitionTime']
>> 7.6

# Multiple values
series['SOPInstanceUID']
>> ["1.123.1241.123124124.12.1",
    "1.123.1241.123124124.12.2",
    "1.123.1241.123124124.12.3"]

# No value
series['MissingKey']
...
KeyError: "The keyword: 'MissingKey' does not exist in the header!"

Another useful feature of the indexing operator is for querying an Image instance based on its index in the series:

series[6]
>> dicom_parser.image.Image
series[6].header['InstanceNumber]
>> 7   # InstanceNumber is 1-indexed

The data property returns a stacked volume of the images’ data:

type(series.data)
>>> numpy.ndarray
series.data.shape
>> (224, 224, 208)

Siemens 4D data

Reading Siemens 4D data encoded as mosaics is also supported:

fmri_series = Series('/path/to/dicom/fmri/')
fmri_series.data.shape
>> (96, 96, 64, 200)