Quickstart¶
The most basic usage case is reading a single DICOM image (.dcm file) as
an Image
instance.
>>> from dicom_parser import Image
>>> 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)