DICOM files parser meant to facilitate data access.
Project description
dicom_parser
dicom_parser is a utility python package meant to facilitate access to DICOM header information by extending the functionality of pydicom.
Essentially, dicom_parser uses DICOM's data-element value-representation (VR), as well as prior knowledge on vendor-specific private tags or encoding schemes, in order to transform them to more "pythonic" data structures when possible.
For more information, please see the documentation.
Installation
To install the latest version of dicom_parser
, simply run:
pip install dicom_parser
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.
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)
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