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Generates dummy medical image data with realistic headers to be used in image handling tests

Project description

https://github.com/australian-imaging-service/medimages4tests/actions/workflows/test.yml/badge.svg https://codecov.io/gh/australian-imaging-service/medimages4tests/branch/main/graph/badge.svg?token=UIS0OGPST7 Supported Python versions Latest Version

Generates dummy medical images, with junk image data but realistic headers, to test imaging handling pipelines

Installation

Medimage4tests is available on PyPI so to install, simply use pip

$ pip3 install medimages4tests

or include in your package’s test_requires.

Usage

Create a pytest fixture consisting of a dummy image with field-map metadata in DICOM format

# Import medimages4tests generator functions
from medimages4tests.dummy.dicom.mri.fmap.ge.discovery_mr888.dv26_0_r05_2008a import get_image

# Return generated images in pytest fixtures (or alternative test framework)
@pytest.fixture()
def ge_dicom_fmap():
    return get_image()

Create a dummy NIfTI image

import numpy
# Import `get_image` function
from medimages4tests.dummy.nifti import get_image

# Create dummy nifti image of 10x10x10 containing all ones
@pytest.fixture()
def ones_nifti():
    return get_image(
        data=numpy.ones((10, 10, 10))
    )

Access real T1-weighted from OpenNeuro.org

from medimages4tests.mri.neuro.t1w import get_image

@pytest.fixture()
def t1w_nifti():
    return get_image(sample="ds004130-ON01016")

Acknowledgements

The authors acknowledge the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability.

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